Oracle University Podcast

Listen to Lois Houston and Nikita Abraham, along with Senior Principal Product Manager Wes Prichard, as they explore the five core components of OCI AI services: language, speech, vision, document understanding, and anomaly detection, to help you make better sense of all that unstructured data around you.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:46 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we spoke about OCI AI Portfolio, including AI and ML services, and the OCI AI infrastructure. Nikita: Yeah, and in today’s episode, we’re going to continue down a similar path and take a closer look at OCI AI services. 01:16 Lois: With us today is Senior Principal Product Manager, Wes Prichard. Hi Wes! It’s lovely to have you here with us. Hemant gave us a broad overview of the various OCI AI services last week, but we’re really hoping to get into each of them with you. So, let’s jump right in and start with the OCI Language service. What can you tell us about it? Wes: OCI Language analyzes unstructured text for you. It provides models trained on industry data to perform language analysis with no data science experience needed.  01:48 Nikita: What kind of big things can it do? Wes: It has five main capabilities. First, it detects the language of the text. It recognizes 75 languages, from Afrikaans to Welsh.  It identifies entities, things like names, places, dates, emails, currency, organizations, phone numbers--14 types in all. It identifies the sentiment of the text, and not just one sentiment for the entire block of text, but the different sentiments for different aspects.  02:17 Nikita: What do you mean by that, Wes? Wes: So let's say you read a restaurant review that said, the food was great, but the service sucked. You'll get food with a positive sentiment and service with a negative sentiment. And it also analyzes the sentiment for every sentence.  Lois: Ah, that’s smart. Ok, so we covered three capabilities. What else? Wes: It identifies key phrases in the text that represent the important ideas or subjects. And it classifies the general topic of the text from a list of 600 categories and subcategories.  02:48 Lois: Ok, and then there’s the OCI Speech service...  Wes: OCI Speech is very straightforward. It locks the data in audio tracks by converting speech to text. Developers can use Oracle's time-tested acoustic language models to provide highly accurate transcription for audio or video files across multiple languages.  OCI Speech automatically transcribes audio and video files into text using advanced deep learning techniques. There's no data science experience required. It processes data directly in object storage. And it generates timestamped, grammatically accurate transcriptions.  03:22 Nikita: What are some of the main features of OCI Speech? Wes: OCI Speech supports multiple languages, specifically English, Spanish, and Portuguese, with more coming in the future. It has batching support where multiple files can be submitted with a single call. It has blazing fast processing. It can transcribe hours of audio in less than 10 minutes. It does this by chunking up your audio into smaller segments, and transcribing each segment, and then joining them all back together into a single file. It provides a confidence score, both per word and per transcription. It punctuates transcriptions to make the text more readable and to allow downstream systems to process the text with less friction.  And it has SRT file support.  04:06 Lois: SRT? What’s that? Wes: SRT is the most popular closed caption output file format. And with this SRT support, users can add closed captions to their video. OCI Speech makes transcribed text more readable to resemble how humans write. This is called normalization. And the service will normalize things like addresses, times, numbers, URLs, and more.  It also does profanity filtering, where it can either remove, mask, or tag profanity and output text, where removing replaces the word with asterisks, and masking does the same thing, but it retains the first letter, and tagging will leave the word in place, but it provides tagging in the output data.  04:49 Nikita: And what about OCI Vision? What are its capabilities? Wes: Vision is a computed vision service that works on images, and it provides two main capabilities-- image analysis and document AI. Image analysis analyzes photographic images. Object detection is the feature that detects objects inside an image using a bounding box and assigning a label to each object with an accuracy percentage. Object detection also locates and extracts text that appears in the scene, like on a sign.  Image classification will assign classification labels to the image by identifying the major features in the scene. One of the most powerful capabilities of image analysis is that, in addition to pretrained models, users can retrain the models with their own unique data to fit their specific needs.  05:40 Lois: So object detection and image classification are features of image analysis. I think I got it! So then what’s document AI?  Wes: It's used for working with document images. You can use it to understand PDFs or document image types, like JPEG, PNG, and Tiff, or photographs containing textual information.  06:01 Lois: And what are its most important features? Wes: The features of document AI are text recognition, also known as OCR or optical character recognition.  And this extracts text from images, including non-trivial scenarios, like handwritten texts, plus tilted, shaded, or rotated documents. Document classification classifies documents into 10 different types based on visual appearance, high-level features, and extracted keywords. This is useful when you need to process a document, based on its classification, like an invoice, a receipt, or a resume.  Language detection analyzes the visual features of text to determine the language rather than relying on the text itself. Table extraction identifies tables in docs and extracts their content in tabular form. Key value extraction finds values for 13 common fields and line items in receipts, things like merchant name and transaction date.  07:02 Want to get the inside scoop on Oracle University? Head over to the Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products. Read the OU Learning Blog. Participate in Challenges. And stay up-to-date with upcoming certification opportunities. Visit mylearn.oracle.com to get started.  07:27 Nikita: Welcome back! Wes, I want to ask you about OCI Anomaly Detection. We discussed it a bit last week and it seems like such an intelligent and efficient service. Wes: Oracle Cloud Infrastructure Anomaly Detection identifies anomalies in time series data. Equipment sensors generate time series data, but all kinds of business metrics are also time-based. The unique feature of this service is that it finds anomalies, not just in a single signal, but across many signals at once. That's important because machines often generate multiple signals at once and the signals are often related.  08:03 Nikita: Ok you need to give us an example of this! Wes: Think of a pump that has an output pressure, a flow rate, an RPM, and an electrical current draw. When a pump's going to fail, anomalies may appear across several of those signals but at different times. OCI Anomaly Detection helps you to identify anomalies in a multivariate data set by taking advantage of the interrelationship among signals.  The service contains algorithms for both multi-signal, as in multivariate, single signal, as in univariate anomaly detection, and it automatically determines which algorithm to use based on the training data provided. The multivariate algorithm is called MSET-2, which stands for Multivariate State Estimation technique, and it's unique to Oracle.  08:49 Lois: And the 2? Wes: The 2 in the name refers to the patented enhancements by Oracle labs that automatically identify and fix data quality issues resulting in fewer false alarms and more accurate results.  Now unlike some of the other AI services, OCI Anomaly Detection is always trained on the customer's data. It's trained using actual historical data with no anomalies, and there can be as many different trained models as needed for different sets of signals.  09:18 Nikita: So where would one use a service like this? Wes: One of the most obvious applications of this service is for predictive maintenance. Early warning of a problem provides the opportunity to deploy maintenance resources and schedule downtime to minimize disruption to the business.  09:33 Lois: How would you train an OCI Anomaly Detection model? Wes: It's a simple four-step process to prepare a model that can be used for anomaly detection. The first step is to obtain training data from the system to be monitored. The data must contain no anomalies and should cover the normal range of values that would be experienced in a full business cycle.  Second, the training data file is uploaded to an object storage bucket.  Third, a data set is created for the training data. So a data set in this context is an object in the OCI Anomaly Detection service to manage data used for training and testing models.  And fourth, the model is trained. A wizard in the user interface steps the user through the required inputs, such as the training data set and some training parameters like the target false alarm probability.  10:23 Lois: How would this service know about the data and whether the trained model is univariate or multivariate? Wes: When training OCI Anomaly Detection models, the user does not need to specify whether the intended model is for multivariate or univariate data. It does this detection automatically.  For example, if a model is trained with 10 signals and 5 of those signals are determined to be correlated enough for multivariate anomaly detection, it will create an internal multivariate model for those signals. If the other five signals are not correlated with each other, it will create an internal univariate model for each one.  From the user's perspective, the result will be a single OCI anomaly detection model for the 10 signals. But internally, the signals are treated differently based on the training. A user can also train a model on a single signal and it will result in a univariate model.  11:16 Lois: What does this OCI Anomaly Detection model training entail? How does it ensure that it does not have any false alarms? Wes: Training a model requires a single data file with no anomalies that should cover a complete business cycle, which means it should represent all the normal variations in the signal. During training, OCI Anomaly Detection will use a portion of the data for training and another portion for automated testing. The fraction used for each is specified when the model is trained.  When model training is complete, it's best practice to do another test of the model with a data set containing anomalies to see if the anomalies are detected and if there are any false alarms. Based on the outcome, the user may want to retrain the model and specify a different false alarm probability, also called F-A-P or FAP. The FAP is the probability that the model would produce a false alarm. The false alarm probability can be thought of as the sensitivity of the model. The lower the false alarm probability, the less likelihood of it reporting a false alarm, but the less sensitive it will be to detecting anomalies. Selecting the right FAP is a business decision based on the need for sensitive detections balanced by the ability to tolerate false alarms.  Once a model has been trained and the user is satisfied with its detection performance, it can then be used for inferencing.  12:44 Nikita: Inferencing? Is that what I think it is?  Wes: New data is submitted to the model and OCI Anomaly Detection will respond with anomalies that are detected. The input data must contain the same signals that the model was trained on. So, for example, if the model was trained on signals A, B, C, and D, then for detection inferencing, the same four signals must be provided. No more, no less. 13:07 Lois: Where can I find the features of OCI Anomaly Detection that you mentioned?  Wes: The training and inferencing features of OCI Anomaly Detection can be accessed through the OCI console. However, a human-driven interface is not efficient for most business scenarios.  In most cases, automating the detection of anomalies through software is preferred to be able to process hundreds or thousands of signals using many trained models. The service provides multiple software interfaces for this purpose.  Each trained model is accessible through a REST API and an HTTP endpoint. Additionally, programming language-specific SDKs are available for multiple languages, including Python. Using the Python SDK, data scientists can work with OCI Anomaly Detection for both training and inferencing in an OCI Data Science notebook.  13:58 Nikita: How can a data scientist take advantage of these capabilities?  Wes: Well, you can write code against the REST API or use any of the various language SDKs. But for data scientists working in OCI Data Science, it makes sense to use Python.  14:12 Lois: That’s exciting! What does it take to use the Python SDK in a notebook… to be able to use the AI services? Wes: You can use a Notebook session in OCI Data Science to invoke the SDK for any of the AI services.  This might be useful to generate new features for a custom model or simply as a way to consume the service using a familiar Python interface. But before you can invoke the SDK, you have to prepare the data science notebook session by supplying it with an API Signing Key.  Signing Key is unique to a particular user and tenancy and authenticates that user to OCI when invoking the SDK. So therefore, you want to make sure you safeguard your Signing Key and never share it with another user.  14:55 Nikita: And where would I get my API Signing Key? Wes: You can obtain an API Signing Key from your user profile in the OCI Console. Then you save that key as a file to your local machine.  The API Signing Key also provides commands to be added to a config file that the SDK expects to find in the environment, where the SDK code is executing. The config file then references the key file. Once these files are prepared on your local machine, you can upload them to the Notebook session, where you will execute SDK code for the AI service.  The API Signing Key and config file can be reused with any of your notebook sessions, and the same files also work for all of the AI services. So, the files only need to be created once for each user and tenancy combination.  15:48 Lois: Thank you so much, Wes, for this really insightful discussion. To learn more about the topics covered today, you can visit mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. Nikita: And remember, that course prepares you for the Oracle Cloud Infrastructure AI Foundations Associate certification that you can take for free! So, don’t wait too long to check it out. Join us next week for another episode of the Oracle University Podcast. Until then, this is Nikita Abraham… Lois Houston: And Lois Houston, signing off! 16:23 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

28 Mai 202416min

Oracle has been actively focusing on bringing AI to the enterprise at every layer of its tech stack, be it SaaS apps, AI services, infrastructure, or data.   In this episode, hosts Lois Houston and Nikita Abraham, along with senior instructors Hemant Gahankari and Himanshu Raj, discuss OCI AI and Machine Learning services. They also go over some key OCI Data Science concepts and responsible AI principles.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:46 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hey everyone! In our last episode, we dove into Generative AI and Language Learning Models.  Lois: Yeah, that was an interesting one. But today, we’re going to discuss the AI and machine learning services offered by Oracle Cloud Infrastructure, and we’ll look at the OCI AI infrastructure. Nikita: I’m also going to try and squeeze in a couple of questions on a topic I’m really keen about, which is responsible AI. To take us through all of this, we have two of our colleagues, Hemant Gahankari and Himanshu Raj. Hemant is a Senior Principal OCI Instructor and Himanshu is a Senior Instructor on AI/ML. So, let’s get started! 01:36 Lois: Hi Hemant! We’re so excited to have you here! We know that Oracle has really been focusing on bringing AI to the enterprise at every layer of our stack.  Hemant: It all begins with data and infrastructure layers. OCI AI services consume data, and AI services, in turn, are consumed by applications.  This approach involves extensive investment from infrastructure to SaaS applications. Generative AI and massive scale models are the more recent steps. Oracle AI is the portfolio of cloud services for helping organizations use the data they may have for the business-specific uses.  Business applications consume AI and ML services. The foundation of AI services and ML services is data. AI services contain pre-built models for specific uses. Some of the AI services are pre-trained, and some can be additionally trained by the customer with their own data.  AI services can be consumed by calling the API for the service, passing in the data to be processed, and the service returns a result. There is no infrastructure to be managed for using AI services.  02:58 Nikita: How do I access OCI AI services? Hemant: OCI AI services provide multiple methods for access. The most common method is the OCI Console. The OCI Console provides an easy to use, browser-based interface that enables access to notebook sessions and all the features of all the data science, as well as AI services.  The REST API provides access to service functionality but requires programming expertise. And API reference is provided in the product documentation. OCI also provides programming language SDKs for Java, Python, TypeScript, JavaScript, .Net, Go, and Ruby. The command line interface provides both quick access and full functionality without the need for scripting.  03:52 Lois: Hemant, what are the types of OCI AI services that are available?  Hemant: OCI AI services is a collection of services with pre-built machine learning models that make it easier for developers to build a variety of business applications. The models can also be custom trained for more accurate business results. The different services provided are digital assistant, language, vision, speech, document understanding, anomaly detection.  04:24 Lois: I know we’re going to talk about them in more detail in the next episode, but can you introduce us to OCI Language, Vision, and Speech? Hemant: OCI Language allows you to perform sophisticated text analysis at scale. Using the pre-trained and custom models, you can process unstructured text to extract insights without data science expertise. Pre-trained models include language detection, sentiment analysis, key phrase extraction, text classification, named entity recognition, and personal identifiable information detection.  Custom models can be trained for named entity recognition and text classification with domain-specific data sets. In text translation, natural machine translation is used to translate text across numerous languages.  Using OCI Vision, you can upload images to detect and classify objects in them. Pre-trained models and custom models are supported. In image analysis, pre-trained models perform object detection, image classification, and optical character recognition. In image analysis, custom models can perform custom object detection by detecting the location of custom objects in an image and providing a bounding box.  The OCI Speech service is used to convert media files to readable texts that's stored in JSON and SRT format. Speech enables you to easily convert media files containing human speech into highly exact text transcriptions.  06:12 Nikita: That’s great. And what about document understanding and anomaly detection? Hemant: Using OCI document understanding, you can upload documents to detect and classify text and objects in them. You can process individual files or batches of documents. In OCR, document understanding can detect and recognize text in a document. In text extraction, document understanding provides the word level and line level text, and the bounding box, coordinates of where the text is found.  In key value extraction, document understanding extracts a predefined list of key value pairs of information from receipts, invoices, passports, and driver IDs. In table extraction, document understanding extracts content in tabular format, maintaining the row and column relationship of cells. In document classification, the document understanding classifies documents into different types.  The OCI Anomaly Detection service is a service that analyzes large volume of multivariate or univariate time series data. The Anomaly Detection service increases the reliability of businesses by monitoring their critical assets and detecting anomalies early with high precision. Anomaly Detection is the identification of rare items, events, or observations in data that differ significantly from the expectation.  07:55 Nikita: Where is Anomaly Detection most useful? Hemant: The Anomaly Detection service is designed to help with analyzing large amounts of data and identifying the anomalies at the earliest possible time with maximum accuracy. Different sectors, such as utility, oil and gas, transportation, manufacturing, telecommunications, banking, and insurance use Anomaly Detection service for their day-to-day activities.  08:23 Lois: Ok…and the first OCI AI service you mentioned was digital assistant… Hemant: Oracle Digital Assistant is a platform that allows you to create and deploy digital assistants, which are AI driven interfaces that help users accomplish a variety of tasks with natural language conversations. When a user engages with the Digital Assistant, the Digital Assistant evaluates the user input and routes the conversation to and from the appropriate skills.  Digital Assistant greets the user upon access. Upon user requests, list what it can do and provide entry points into the given skills. It routes explicit user requests to the appropriate skills. And it also handles interruptions to flows and disambiguation. It also handles requests to exit the bot.  09:21 Nikita: Excellent! Let’s bring Himanshu in to tell us about machine learning services. Hi Himanshu! Let’s talk about OCI Data Science. Can you tell us a bit about it? Himanshu: OCI Data Science is the cloud service focused on serving the data scientist throughout the full machine learning life cycle with support for Python and open source.  The service has many features, such as model catalog, projects, JupyterLab notebook, model deployment, model training, management, model explanation, open source libraries, and AutoML.  09:56 Lois: Himanshu, what are the core principles of OCI Data Science?  Himanshu: There are three core principles of OCI Data Science. The first one, accelerated. The first principle is about accelerating the work of the individual data scientist. OCI Data Science provides data scientists with open source libraries along with easy access to a range of compute power without having to manage any infrastructure. It also includes Oracle's own library to help streamline many aspects of their work.  The second principle is collaborative. It goes beyond an individual data scientist’s productivity to enable data science teams to work together. This is done through the sharing of assets, reducing duplicative work, and putting reproducibility and auditability of models for collaboration and risk management.  Third is enterprise grade. That means it's integrated with all the OCI Security and access protocols. The underlying infrastructure is fully managed. The customer does not have to think about provisioning compute and storage. And the service handles all the maintenance, patching, and upgrades so user can focus on solving business problems with data science.  11:11 Nikita: Let’s drill down into the specifics of OCI Data Science. So far, we know it’s cloud service to rapidly build, train, deploy, and manage machine learning models. But who can use it? Where is it? And how is it used? Himanshu: It serves data scientists and data science teams throughout the full machine learning life cycle.  Users work in a familiar JupyterLab notebook interface, where they write Python code. And how it is used? So users preserve their models in the model catalog and deploy their models to a managed infrastructure.  11:46 Lois: Walk us through some of the key terminology that’s used. Himanshu: Some of the important product terminology of OCI Data Science are projects. The projects are containers that enable data science teams to organize their work. They represent collaborative work spaces for organizing and documenting data science assets, such as notebook sessions and models.  Note that tenancy can have as many projects as needed without limits. Now, this notebook session is where the data scientists work. Notebook sessions provide a JupyterLab environment with pre-installed open source libraries and the ability to add others. Notebook sessions are interactive coding environment for building and training models.  Notebook sessions run in a managed infrastructure and the user can select CPU or GPU, the compute shape, and amount of storage without having to do any manual provisioning. The other important feature is Conda environment. It's an open source environment and package management system and was created for Python programs.  12:53 Nikita: What is a Conda environment used for? Himanshu: It is used in the service to quickly install, run, and update packages and their dependencies. Conda easily creates, saves, loads, and switches between environments in your notebooks sessions. 13:07 Nikita: Earlier, you spoke about the support for Python in OCI Data Science. Is there a dedicated library? Himanshu: Oracle's Accelerated Data Science ADS SDK is a Python library that is included as part of OCI Data Science.  ADS has many functions and objects that automate or simplify the steps in the data science workflow, including connecting to data, exploring, and visualizing data. Training a model with AutoML, evaluating models, and explaining models. In addition, ADS provides a simple interface to access the data science service mode model catalog and other OCI services, including object storage.  13:45 Lois: I also hear a lot about models. What are models? Himanshu: Models define a mathematical representation of your data and business process. You create models in notebooks, sessions, inside projects.  13:57 Lois: What are some other important terminologies related to models? Himanshu: The next terminology is model catalog. The model catalog is a place to store, track, share, and manage models.  The model catalog is a centralized and managed repository of model artifacts. A stored model includes metadata about the provenance of the model, including Git-related information and the script. Our notebook used to push the model to the catalog. Models stored in the model catalog can be shared across members of a team, and they can be loaded back into a notebook session.  The next one is model deployments. Model deployments allow you to deploy models stored in the model catalog as HTTP endpoints on managed infrastructure.  14:45 Lois: So, how do you operationalize these models? Himanshu: Deploying machine learning models as web applications, HTTP API endpoints, serving predictions in real time is the most common way to operationalize models. HTTP endpoints or the API endpoints are flexible and can serve requests for the model predictions. Data science jobs enable you to define and run a repeatable machine learning tasks on fully managed infrastructure.  Nikita: Thanks for that, Himanshu.  15:18 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security, artificial intelligence, and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started.  15:46 Nikita: Welcome back! The Oracle AI Stack consists of AI services and machine learning services, and these services are built using AI infrastructure. So, let’s move on to that. Hemant, what are the components of OCI AI Infrastructure? Hemant: OCI AI Infrastructure is mainly composed of GPU-based instances. Instances can be virtual machines or bare metal machines. High performance cluster networking that allows instances to communicate to each other. Super clusters are a massive network of GPU instances with multiple petabytes per second of bandwidth. And a variety of fully managed storage options from a single byte to exabytes without upfront provisioning are also available.  16:35 Lois: Can we explore each of these components a little more? First, tell us, why do we need GPUs? Hemant: ML and AI needs lots of repetitive computations to be made on huge amounts of data. Parallel computing on GPUs is designed for many processes at the same time. A GPU is a piece of hardware that is incredibly good in performing computations.  GPU has thousands of lightweight cores, all working on their share of data in parallel. This gives them the ability to crunch through extremely large data set at tremendous speed.  17:14 Nikita: And what are the GPU instances offered by OCI? Hemant: GPU instances are ideally suited for model training and inference. Bare metal and virtual machine compute instances powered by NVIDIA GPUs H100, A100, A10, and V100 are made available by OCI.  17:35 Nikita: So how do we choose what to train from these different GPU options?  Hemant: For large scale AI training, data analytics, and high performance computing, bare metal instances BM 8 X NVIDIA H100 and BM 8 X NVIDIA A100 can be used.  These provide up to nine times faster AI training and 30 times higher acceleration for AI inferencing. The other bare metal and virtual machines are used for small AI training, inference, streaming, gaming, and virtual desktop infrastructure.  18:14 Lois: And why would someone choose the OCI AI stack over its counterparts? Hemant: Oracle offers all the features and is the most cost effective option when compared to its counterparts.  For example, BM GPU 4.8 version 2 instance costs just $4 per hour and is used by many customers.  Superclusters are a massive network with multiple petabytes per second of bandwidth. It can scale up to 4,096 OCI bare metal instances with 32,768 GPUs.  We also have a choice of bare metal A100 or H100 GPU instances, and we can select a variety of storage options, like object store, or block store, or even file system. For networking speeds, we can reach 1,600 GB per second with A100 GPUs and 3,200 GB per second with H100 GPUs.  With OCI storage, we can select local SSD up to four NVMe drives, block storage up to 32 terabytes per volume, object storage up to 10 terabytes per object, file systems up to eight exabyte per file system. OCI File system employs five replicated storage located in different fault domains to provide redundancy for resilient data protection.  HPC file systems, such as BeeGFS and many others are also offered. OCI HPC file systems are available on Oracle Cloud Marketplace and make it easy to deploy a variety of high performance file servers.  20:11 Lois: I think a discussion on AI would be incomplete if we don’t talk about responsible AI. We’re using AI more and more every day, but can we actually trust it? Hemant: For us to trust AI, it must be driven by ethics that guide us as well. Nikita: And do we have some principles that guide the use of AI? Hemant: AI should be lawful, complying with all applicable laws and regulations. AI should be ethical, that is it should ensure adherence to ethical principles and values that we uphold as humans. And AI should be robust, both from a technical and social perspective. Because even with the good intentions, AI systems can cause unintentional harm. AI systems do not operate in a lawless world. A number of legally binding rules at national and international level apply or are relevant to the development, deployment, and use of AI systems today. The law not only prohibits certain actions but also enables others, like protecting rights of minorities or protecting environment. Besides horizontally applicable rules, various domain-specific rules exist that apply to particular AI applications. For instance, the medical device regulation in the health care sector.  In AI context, equality entails that the systems’ operations cannot generate unfairly biased outputs. And while we adopt AI, citizens right should also be protected.  21:50 Lois: Ok, but how do we derive AI ethics from these? Hemant: There are three main principles.  AI should be used to help humans and allow for oversight. It should never cause physical or social harm. Decisions taken by AI should be transparent and fair, and also should be explainable. AI that follows the AI ethical principles is responsible AI.  So if we map the AI ethical principles to responsible AI requirements, these will be like, AI systems should follow human-centric design principles and leave meaningful opportunity for human choice. This means securing human oversight. AI systems and environments in which they operate must be safe and secure, they must be technically robust, and should not be open to malicious use.  The development, and deployment, and use of AI systems must be fair, ensuring equal and just distribution of both benefits and costs. AI should be free from unfair bias and discrimination. Decisions taken by AI to the extent possible should be explainable to those directly and indirectly affected.  23:21 Nikita: This is all great, but what does a typical responsible AI implementation process look like?  Hemant: First, a governance needs to be put in place. Second, develop a set of policies and procedures to be followed. And once implemented, ensure compliance by regular monitoring and evaluation.  Lois: And this is all managed by developers? Hemant: Typical roles that are involved in the implementation cycles are developers, deployers, and end users of the AI.  23:56 Nikita: Can we talk about AI specifically in health care? How do we ensure that there is fairness and no bias? Hemant: AI systems are only as good as the data that they are trained on. If that data is predominantly from one gender or racial group, the AI systems might not perform as well on data from other groups.  24:21 Lois: Yeah, and there’s also the issue of ensuring transparency, right? Hemant: AI systems often make decisions based on complex algorithms that are difficult for humans to understand. As a result, patients and health care providers can have difficulty trusting the decisions made by the AI. AI systems must be regularly evaluated to ensure that they are performing as intended and not causing harm to patients.  24:49 Nikita: Thank you, Hemant and Himanshu, for this really insightful session. If you’re interested in learning more about the topics we discussed today, head on over to mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course.  Lois: That’s right, Niki. You’ll find demos that you watch as well as skill checks that you can attempt to better your understanding. In our next episode, we’ll get into the OCI AI Services we discussed today and talk about them in more detail. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 25:25 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

21 Mai 202416min

In this week’s episode, Lois Houston and Nikita Abraham, along with Senior Instructor Himanshu Raj, take you through the extraordinary capabilities of Generative AI, a subset of deep learning that doesn’t make predictions but rather creates its own content.   They also explore the workings of Large Language Models.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:46 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor.  Nikita: Hi everyone! In our last episode, we went over the basics of deep learning. Today, we’ll look at generative AI and large language models, and discuss how they work. To help us with that, we have Himanshu Raj, Senior Instructor on AI/ML. So, let’s jump right in. Hi Himanshu, what is generative AI?  01:21 Himanshu: Generative AI refers to a type of AI that can create new content. It is a subset of deep learning, where the models are trained not to make predictions but rather to generate output on their own.  Think of generative AI as an artist who looks at a lot of paintings and learns the patterns and styles present in them. Once it has learned these patterns, it can generate new paintings that resembles what it learned. 01:48 Lois: Let's take an example to understand this better. Suppose we want to train a generative AI model to draw a dog. How would we achieve this? Himanshu: You would start by giving it a lot of pictures of dogs to learn from. The AI does not know anything about what a dog looks like. But by looking at these pictures, it starts to figure out common patterns and features, like dogs often have pointy ears, narrow faces, whiskers, etc. You can then ask it to draw a new picture of a dog.  The AI will use the patterns it learned to generate a picture that hopefully looks like a dog. But remember, the AI is not copying any of the pictures it has seen before but creating a new image based on the patterns it has learned. This is the basic idea behind generative AI. In practice, the process involves a lot of complex maths and computation, and there are different techniques and architectures that can be used, such as variational autoencoders (VAs) and Generative Adversarial Networks (GANs).  02:48 Nikita: Himanshu, where is generative AI used in the real world? Himanshu: Generative AI models have a wide variety of applications across numerous domains. For the image generation, generative models like GANs are used to generate realistic images. They can be used for tasks, like creating artwork, synthesizing images of human faces, or transforming sketches into photorealistic images.  For text generation, large language models like GPT 3, which are generative in nature, can create human-like text. This has applications in content creation, like writing articles, generating ideas, and again, conversational AI, like chat bots, customer service agents. They are also used in programming for code generation and debugging, and much more.  For music generation, generative AI models can also be used. They create new pieces of music after being trained on a specific style or collection of tunes. A famous example is OpenAI's MuseNet. 03:42 Lois: You mentioned large language models in the context of text-based generative AI. So, let’s talk a little more about it. Himanshu, what exactly are large language models? Himanshu: LLMs are a type of artificial intelligence models built to understand, generate, and process human language at a massive scale. They were primarily designed for sequence to sequence tasks such as machine translation, where an input sequence is transformed into an output sequence.  LLMs can be used to translate text from one language to another. For example, an LLM could be used to translate English text into French. To do this job, LLM is trained on a massive data set of text and code which allows it to learn the patterns and relationships that exist between different languages. The LLM translates, “How are you?” from English to French, “Comment allez-vous?”  It can also answer questions like, what is the capital of France? And it would answer the capital of France is Paris. And it will write an essay on a given topic. For example, write an essay on French Revolution, and it will come up with a response like with a title and introduction. 04:53 Lois: And how do LLMs actually work? Himanshu: So, LLM models are typically based on deep learning architectures such as transformers. They are also trained on vast amount of text data to learn language patterns and relationships, again, with a massive number of parameters usually in order of millions or even billions. LLMs have also the ability to comprehend and understand natural language text at a semantic level. They can grasp context, infer meaning, and identify relationships between words and phrases.  05:26 Nikita: What are the most important factors for a large language model? Himanshu: Model size and parameters are crucial aspects of large language models and other deep learning models. They significantly impact the model’s capabilities, performance, and resource requirement. So, what is model size? The model size refers to the amount of memory required to store the model's parameter and other data structures. Larger model sizes generally led to better performance as they can capture more complex patterns and representation from the data.  The parameters are the numerical values of the model that change as it learns to minimize the model's error on the given task. In the context of LLMs, parameters refer to the weights and biases of the model's transformer layers. Parameters are usually measured in terms of millions or billions. For example, GPT-3, one of the largest LLMs to date, has 175 billion parameters making it extremely powerful in language understanding and generation.  Tokens represent the individual units into which a piece of text is divided during the processing by the model. In natural language, tokens are usually words, subwords, or characters. Some models have a maximum token limit that they can process and longer text can may require truncation or splitting. Again, balancing model size, parameters, and token handling is crucial when working with LLMs.  06:49 Nikita: But what’s so great about LLMs? Himanshu: Large language models can understand and interpret human language more accurately and contextually. They can comprehend complex sentence structures, nuances, and word meanings, enabling them to provide more accurate and relevant responses to user queries. This model can generate human-like text that is coherent and contextually appropriate. This capability is valuable for context creation, automated writing, and generating personalized response in applications like chatbots and virtual assistants. They can perform a variety of tasks.  Large language models are very versatile and adaptable to various industries. They can be customized to excel in applications such as language translation, sentiment analysis, code generation, and much more. LLMs can handle multiple languages making them valuable for cross-lingual tasks like translation, sentiment analysis, and understanding diverse global content.  Large language models can be again, fine-tuned for a specific task using a minimal amount of domain data. The efficiency of LLMs usually grows with more data and parameters. 07:55 Lois: You mentioned the “sequence to sequence tasks” earlier. Can you explain the concept in simple terms for us? Himanshu: Understanding language is difficult for computers and AI systems. The reason being that words often have meanings based on context. Consider a sentence such as Jane threw the frisbee, and her dog fetched it.  In this sentence, there are a few things that relate to each other. Jane is doing the throwing. The dog is doing the fetching. And it refers to the frisbee. Suppose we are looking at the word “it” in the sentence. As a human, we understand easily that “it” refers to the frisbee. But for a machine, it can be tricky. The goal in sequence problems is to find patterns, dependencies, or relationships within the data and make predictions, classification, or generate new sequences based on that understanding. 08:48 Lois: And where are sequence models mostly used? Himanshu: Some common example of sequence models includes natural language processing, which we call NLP, tasks such as machine translation, text generation sentiment analysis, language modeling involve dealing with sequences of words or characters.  Speech recognition. Converting audio signals into text, involves working with sequences of phonemes or subword units to recognize spoken words. Music generation. Generating new music involves modeling musical sequences, nodes, and rhythms to create original compositions.  Gesture recognition. Sequences of motion or hand gestures are used to interpret human movements for applications, such as sign language recognition or gesture-based interfaces. Time series analysis. In fields such as finance, economics, weather forecasting, and signal processing, time series data is used to predict future values, detect anomalies, and understand patterns in temporal data. 09:56 The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community. Visit mylearn.oracle.com to get started.  10:23 Nikita: Welcome back! Himanshu, what would be the best way to solve those sequence problems you mentioned? Let’s use the same sentence, “Jane threw the frisbee, and her dog fetched it” as an example. Himanshu: The solution is transformers. It's like model has a bird's eye view of the entire sentence and can see how all the words relate to each other. This allows it to understand the sentence as a whole instead of just a series of individual words. Transformers with their self-attention mechanism can look at all the words in the sentence at the same time and understand how they relate to each other.  For example, transformer can simultaneously understand the connections between Jane and dog even though they are far apart in the sentence. 11:13 Nikita: But how? Himanshu: The answer is attention, which adds context to the text. Attention would notice dog comes after frisbee, fetched comes after dog, and it comes after fetched.  Transformer does not look at it in isolation. Instead, it also pays attention to all the other words in the sentence at the same time. But considering all these connections, the model can figure out that “it” likely refers to the frisbee.  The most famous current models that are emerging in natural language processing tasks consist of dozens of transformers or some of their variants, for example, GPT or Bert. 11:53 Lois: I was looking at the AI Foundations course on MyLearn and came across the terms “prompt engineering” and “fine tuning.” Can you shed some light on them? Himanshu: A prompt is the input or initial text provided to the model to elicit a specific response or behavior. So, this is something which you write or ask to a language model. Now, what is prompt engineering? So prompt engineering is the process of designing and formulating specific instructions or queries to interact with a large language model effectively.  In the context of large language models, such as GPT 3 or Burt, prompts are the input text or questions given to the model to generate responses or perform specific tasks.  The goal of prompt engineering is to ensure that the language model understands the user's intent correctly and provide accurate and relevant responses. 12:47 Nikita: That sounds easy enough, but fine tuning seems a bit more complex. Can you explain it with an example? Himanshu: Imagine you have a versatile recipe robot named chef bot. Suppose that chef bot is designed to create delicious recipes for any dish you desire.  Chef bot recognizes the prompt as a request for a pizza recipe, and it knows exactly what to do. However, if you want chef bot to be an expert in a particular type of cuisine, such as Italian dishes, you fine-tune chef bot for Italian cuisine by immersing it in a culinary crash course filled with Italian cookbooks, traditional Italian recipes, and even Italian cooking shows.  During this process, chef bot becomes more specialized in creating authentic Italian recipes, and this option is called fine tuning. LLMs are general purpose models that are pre-trained on large data sets but are often fine-tuned to address specific use cases.  When you combine prompt engineering and fine tuning, and you get a culinary wizard in chef bot, a recipe robot that is not only great at understanding specific dish requests but also capable of following a specific dish requests and even mastering the art of cooking in a particular culinary style. 14:08 Lois: Great! Now that we’ve spoken about all the major components, can you walk us through the life cycle of a large language model? Himanshu: The life cycle of a Large Language Model, LLM, involves several stages, from its initial pre-training to its deployment and ongoing refinement.  The first of this lifecycle is pre-training. The LLM is initially pre-trained on a large corpus of text data from the internet. During pre-training, the model learns grammar, facts, reasoning abilities, and general language understanding. The model predicts the next word in a sentence given the previous words, which helps it capture relationships between words and the structure of language.  The second phase is fine tuning initialization. After pre-training, the model's weights are initialized, and it's ready for task-specific fine tuning. Fine tuning can involve supervised learning on labeled data for specific tasks, such as sentiment analysis, translation, or text generation.  The model is fine-tuned on specific tasks using a smaller domain-specific data set. The weights from pre-training are updated based on the new data, making the model task aware and specialized. The next phase of the LLM life cycle is prompt engineering. So this phase craft effective prompts to guide the model's behavior in generating specific responses.  Different prompt formulations, instructions, or context can be used to shape the output.  15:34 Nikita: Ok… we’re with you so far. What’s next? Himanshu: The next phase is evaluation and iteration. So models are evaluated using various metrics to access their performance on specific tasks. Iterative refinement involves adjusting model parameters, prompts, and fine tuning strategies to improve results.  So as a part of this step, you also do few shot and one shot inference. If needed, you further fine tune the model with a small number of examples. Basically, few shot or a single example, one shot for new tasks or scenarios.  Also, you do the bias mitigation and consider the ethical concerns. These biases and ethical concerns may arise in models output. You need to implement measures to ensure fairness in inclusivity and responsible use.  16:28 Himanshu: The next phase in LLM life cycle is deployment. Once the model has been fine-tuned and evaluated, it is deployed for real world applications. Deployed models can perform tasks, such as text generation, translation, summarization, and much more. You also perform monitoring and maintenance in this phase.  So you continuously monitor the model's performance and output to ensure it aligns with desired outcomes. You also periodically update and retrain the model to incorporate new data and to adapt to evolving language patterns. This overall life cycle can also consist of a feedback loop, whether you gather feedbacks from users and incorporate it into the model’s improvement process.  You use this feedback to further refine prompts, fine tuning, and overall model behavior. RLHF, which is Reinforcement Learning with Human Feedback, is a very good example of this feedback loop. You also research and innovate as a part of this life cycle, where you continue to research and develop new techniques to enhance the model capability and address different challenges associated with it. 17:40 Nikita: As we’re talking about the LLM life cycle, I see that fine tuning is not only about making an LLM task specific. So, what are some other reasons you would fine tune an LLM model? Himanshu: The first one is task-specific adaptation. Pre-trained language models are trained on extensive and diverse data sets and have good general language understanding. They excel in language generation and comprehension tasks, though the broad understanding of language may not lead to optimal performance in specific task.  These models are not task specific. So the solution is fine tuning. The fine tuning process customizes the pre-trained models for a specific task by further training on task-specific data to adapt the model's knowledge.  The second reason is domain-specific vocabulary. Pre-trained models might lack knowledge of specific words and phrases essential for certain tasks in fields, such as legal, medical, finance, and technical domains. This can limit their performance when applied to domain-specific data.  Fine tuning enables the model to adapt and learn domain-specific words and phrases. These words could be, again, from different domains.  18:56 Himanshu: The third reason to fine tune is efficiency and resource utilization. So fine tuning is computationally efficient compared to training from scratch.  Fine tuning reuses the knowledge from pre-trained models, saving time and resources. Fine tuning requires fewer iterations to achieve task-specific competence. Shorter training cycles expedite the model development process. It conserves computational resources, such as GPU memory and processing power.  Fine tuning is efficient in quicker model deployment. It has faster time to production for real world applications. Fine tuning is, again, scalable, enabling adaptation to various tasks with the same base model, which further reduce resource demands, and it leads to cost saving for research and development.  The fourth reason to fine tune is of ethical concerns. Pre-trained models learns from diverse data. And those potentially inherit different biases. Fine tune might not completely eliminate biases. But careful curation of task-specific data ensures avoiding biased or harmful vocabulary. The responsible uses of domain-specific terms promotes ethical AI applications.  20:14 Lois: Thank you so much, Himanshu, for spending time with us. We had such a great time learning from you. If you want to learn more about the topics discussed today, head over to mylearn.oracle.com and get started on our free AI Foundations course. Nikita: Yeah, we even have a detailed walkthrough of the architecture of transformers that you might want to check out. Join us next week for a discussion on the OCI AI Portfolio. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 20:44 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

14 Mai 202421min

Did you know that the concept of deep learning goes way back to the 1950s? However, it is only in recent years that this technology has created a tremendous amount of buzz (and for good reason!). A subset of machine learning, deep learning is inspired by the structure of the human brain, making it fascinating to learn about.   In this episode, Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Hemant Gahankari about deep learning concepts, including how Convolution Neural Networks work, and help you get your deep learning basics right.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular  Oracle technologies. Let’s get started! 00:47 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal Technical Editor. Nikita: Hi everyone!  Lois: Today, we’re going to focus on the basics of deep learning with our Senior Principal OCI Instructor, Hemant Gahankari. Nikita: Hi Hemant! Thanks for being with us today. So, to get started, what is deep learning? 01:14 Hemant: Hi Niki and hi Lois. So, deep learning is a subset of machine learning that focuses on training Artificial Neural Networks, abbreviated as ANN, to solve a task at hand. Say, for example, image classification. A very important quality of the ANN is that it can process raw data like pixels of an image and extract patterns from it. These patterns are treated as features to predict the outcomes.  Let us say we have a set of handwritten images of digits 0 to 9. As we know, everyone writes the digits in a slightly different way. So how do we train a machine to identify a handwritten digit? For this, we use ANN.  ANN accepts image pixels as inputs, extracts patterns like edges and curves and so on, and correlates these patterns to predict an outcome. That is what digit does the image has in this case.  02:17 Lois: Ok, so what you’re saying is given a bunch of pixels, ANN is able to process pixel data, learn an internal representation of the data, and predict outcomes. That’s so cool! So, why do we need deep learning? Hemant: We need to specify features while we train machine learning algorithm. With deep learning, features are automatically extracted from the data. Internal representation of features and their combinations is built to predict outcomes by deep learning algorithms. This may not be feasible manually.  Deep learning algorithms can make use of parallel computations. For this, usually data is split into small batches and processed parallelly. So these algorithms can process large amount of data in a short time to learn the features and their combinations. This leads to scalability and performance. In short, deep learning complements machine learning algorithms for complex data for which features cannot be described easily.  03:21 Nikita: What can you tell us about the origins of deep learning? Hemant: Some of the deep learning concepts like artificial neuron, perceptron, and multilayer perceptron existed as early as 1950s. One of the most important concept of using backpropagation for training ANN came in 1980s.  In 1990s, convolutional neural networks were also introduced for image analysis tasks. Starting 2000, GPUs were introduced. And 2010 onwards, GPUs became cheaper and widely available. This fueled the widespread adoption of deep learning uses like computer vision, natural language processing, speech recognition, text translation, and so on.  In 2012, major networks like AlexNet and Deep-Q Network were built. 2016 onward, generative use cases of the deep learning also started to come up. Today, we have widely adopted deep learning for a variety of use cases, including large language models and many other types of generative models.  04:32 Lois: Hemant, what are various applications of deep learning algorithms?  Hemant: Deep learning algorithms are targeted at a variety of data and applications. For data, we have images, videos, text, and audio. For images, applications can be image classification, object detection, and segmentation. For textual data, applications are to translate the text or detect a sentiment of a text. For audio, the applications can be music generation, speech to text, and so on.  05:05 Lois: It's important that we select the right deep learning algorithm based on the data and application, right? So how do we do that?  Hemant: For image tasks like image classification, object detection, image segmentation, or facial recognition, CNN is a suitable architecture. For text, we have a choice of the latest transformers or LSTM or even RNN. For generative tasks like text summarization or question answering, transformers is a good choice. For generating images, text to image generation, transformers, GANs, or diffusion models are available choices. 05:45 Nikita: Let’s dive a little deeper into Artificial Neural Networks. Can you tell us more about them, Hemant? Hemant: Artificial Neural Networks are inspired by the human brain. They are made up of interconnected nodes called as neurons.  Nikita: And how are inputs processed by a neuron?  Hemant: In ANN, we assign weights to the connection between neurons. Weighted inputs are added up. And if the sum crosses a specified threshold, the neuron is fired. And the outputs of a layer of neuron become an input to another layer.  06:16 Lois: Hemant, tell us about the building blocks of ANN so we understand this better. Hemant: So first building block is layers. We have input layer, output layer, and multiple hidden layers. The input layer and output layer are mandatory. And the hidden layers are optional. The layers consist of neurons. Neurons are computational units, which accept an input and produce an output.  Weights determine the strength of connection between neurons. So the connections could be between input and a neuron, or it could be between a neuron and another neuron. Activation functions work on the weighted sum of inputs to the neuron and produce an output. Additional input to the neuron that allows a certain degree of flexibility is called as a bias.  07:05 Nikita: I think we’ve got the components of ANN straight but maybe you should give us an example. You mentioned this example earlier…of needing to train ANN to recognize handwritten digits from images. How would we go about that? Hemant: For that, we have to collect a large number of digit images, and we need to train ANN using these images.  So, in this case, the images consist of 28 by 28 pixels, which act as input layer. For the output, we have 10 neurons which represent digits 0 to 9. And we have multiple hidden layers. So, for example, we have two hidden layers which are consisting of 16 neurons each.  The hidden layers are responsible for capturing the internal representation of the raw images. And the output layer is responsible for producing the desired outcomes. So, in this case, the desired outcome is the prediction of whether the digit is 0 or 1 or up to digit 9.  So how do we train this particular ANN? So the first thing we use is the backpropagation algorithm. During training, we show an image to the ANN. Let’s say it is an image of digit 2. So we expect output neuron for digit 2 to fire. But in real, let’s say output neuron of a digit 6 fired.  08:28 Lois: So, then, what do we do?  Hemant: We know that there is an error. So we correct an error. We adjust the weights of the connection between neurons based on a calculation, which we call as backpropagation algorithm. By showing thousands of images and adjusting the weights iteratively, ANN is able to predict correct outcomes for most of the input images. This process of adjusting weights through backpropagation is called as model training.  09:01 Do you have an idea for a new course or learning opportunity? We’d love to hear it! Visit the Oracle University Learning Community and share your thoughts with us on the Idea Incubator. Your suggestion could find a place in future development projects! Visit mylearn.oracle.com to get started.  09:22 Nikita: Welcome back! Let’s move on to CNN. Hemant, what is a Convolutional Neural Network?  Hemant: CNN is a type of deep learning model specifically designed for processing and analyzing grid-like data, such as images and videos. In the ANN, the input image is converted to a single dimensional array and given as an input to the network.   But that does not work well with the image data because image data is inherently two dimensional. CNN works better with the two dimensional data. The role of the CNN is to reduce the images into a form, which is easier to process and without losing features, which are critical for getting a good prediction.  10:10 Lois: A CNN has different layers, right? Could you tell us a bit about them?  Hemant: The first one is input layer. Input layer is followed by feature extraction layers, which is a combination and repetition of convolutional layer with ReLu activation and a pooling layer.  And this is followed by a classification layer. These are the fully connected output layers, where the classification occurs as output classes. The class with the highest probability is the predicted class. And finally, we have the dropout layer. This layer is a regularization technique used to prevent overfitting in the network.  10:51 Nikita: And what are the top applications of CNN? Hemant: One of the most widely used applications of CNNs is image classification. For example, classifying whether an image contains a specific object, say cat or a dog.  CNNs are also used for object detection tasks. The goal here is to draw bounding boxes around objects in an image. CNNs can perform pixel-level segmentation, where each pixel in the image is labeled to represent different objects or regions. CNNs are employed for face recognition tasks as well, identifying and verifying individuals based on facial features.  CNNs are widely used in medical image analysis, helping with tasks like tumor detection, diagnosis, and classification of various medical conditions. CNNs play an important role in the development of self-driving cars, helping them to recognize and understand the road traffic signs, pedestrians, and other vehicles.  12:02 Nikita: Hemant, let’s talk about sequence models. What are they and what are they used for? Hemant: Sequence models are used to solve problems, where the input data is in the form of sequences. The sequences are ordered lists of data points or events.  The goal in sequence models is to find patterns and dependencies within the data and make predictions, classifications, or even generate new sequences.  12:31 Lois: Can you give us some examples of sequence models?  Hemant: Some common examples of the sequence models are in natural language processing, deep learning models are used for tasks, such as machine translation, sentiment analysis, or text generation. In speech recognition, deep learning models are used to convert a recorded audio into text.  Deep learning models can generate new music or create original compositions. Even sequences of hand gestures are interpreted by deep learning models for applications like sign language recognition. In fields like finance or weather prediction, time series data is used to predict future values.  13:15 Nikita: Which deep learning models can be used to work with sequence data?  Hemant: Recurrent Neural Networks, abbreviated as RNNs, are a class of neural network architectures specifically designed to handle sequential data. Unlike traditional feedforward neural network, RNNs have a feedback loop that allows information to persist across different timesteps.  The key features of RNNs is their ability to maintain an internal state often referred to as a hidden state or memory, which is updated as the network processes each element in the input sequence. The hidden state is used as input to the network for the next time step, allowing the model to capture dependencies and patterns in the data that are spread across time.  14:07 Nikita: Are there various types of RNNs? Hemant: There are different types of RNN architectures based on application.  One of them is one to one. This is like feed forward neural network and is not suited for sequential data. A one to many model produces multiple output values for one input value. Music generation or sequence generation are some applications using this architecture.  A many to one model produces one output value after receiving multiple input values. Example is sentiments analysis based on the review. Many to many model produces multiple output values for multiple input values. Examples are machine translation and named entity recognition.  RNN does not perform well when it comes to capturing long-term dependencies. This is due to the vanishing gradients problem, which is overcome by using LSTM model.  15:11 Lois: Another acronym. What is LSTM, Hemant? Hemant: Long Short-Term memory, abbreviated as LSTM, works by using a specialized memory cell and a gating mechanism to capture long term dependencies in the sequential data.  The key idea behind LSTM is to selectively remember or forget information over time, enabling the model to maintain relevant information over long sequences, which helps overcome the vanishing gradients problem.  15:45 Nikita: Can you take us, step-by-step, through the working of LSTM?  Hemant: At each timestep, the LSTM takes an input vector representing the current data point in the sequence. The LSTM also receives the previous hidden state and cell state. These represent what the LSTM has remembered and forgotten up to the current point in the sequence.  The core of the LSTM lies in its gating mechanisms, which include three gates: the input gate, the forget gate, and the output gate. These gates are like the filters that control the flow of information within the LSTM cell. The input gate decides what new information from the current input should be added to the memory cell.  The forget gate determines what information in the current memory cell should be discarded or forgotten. The output gate regulates how much of the current memory cell should be exposed as the output of the current time step.  16:52 Lois: Thank you, Hemant, for joining us in this episode of the Oracle University Podcast. I learned so much today. If you want to learn more about deep learning, visit mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course.  And remember, the AI Foundations course and certification are free. So why not get started now? Nikita: Right, Lois. In our next episode, we will discuss generative AI and language learning models. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 17:26 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

7 Mai 202417min

Does machine learning feel like too convoluted a topic? Not anymore!   Listen to hosts Lois Houston and Nikita Abraham, along with Senior Principal OCI Instructor Hemant Gahankari, talk about foundational machine learning concepts and dive into how supervised learning, unsupervised learning, and reinforcement learning work.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:47 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Principal  Technical Editor.  Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence and we’re going to take it a step further today by talking about some foundational machine learning concepts. After that, we’ll discuss the three main types of machine learning models: supervised learning, unsupervised learning, and reinforcement learning.  01:18 Lois: Hemant Gahankari, a Senior Principal OCI Instructor, joins us for this episode. Hi Hemant! Let’s dive right in. What is machine learning? How does it work? Hemant: Machine learning is a subset of artificial intelligence that focuses on creating computer systems that can learn and predict outcomes from given examples without being explicitly programmed. It is powered by algorithms that incorporate intelligence into machines by automatically learning from a set of examples usually provided as data.  01:54 Nikita: Give us a few examples of machine learning… so we can see what it can do for us. Hemant: Machine learning is used by all of us in our day-to-day life.  When we shop online, we get product recommendations based on our preferences and our shopping history. This is powered by machine learning.  We are notified about movies recommendations based on our viewing history and choices of other similar viewers. This too is driven by machine learning.  While browsing emails, we are warned of a spam mail because machine learning classifies whether the mail is spam or not based on its content. In the increasingly popular self-driving cars, machine learning is responsible for taking the car to its destination.  02:45 Lois: So, how does machine learning actually work? Hemant: Let us say we have a computer and we need to teach the computer to differentiate between a cat and a dog. We do this by describing features of a cat or a dog.  Dogs and cats have distinguishing features. For example, the body color, texture, eye color are some of the defining features which can be used to differentiate a cat from a dog. These are collectively called as input data.  We also provide a corresponding output, which is called as a label, which can be a dog or a cat in this case. By describing a specific set of features, we can say that it is a cat or a dog.  Machine learning model is first trained with the data set. Training data set consists of a set of features and output labels, and is given as an input to the machine learning model.  During the process of training, machine learning model learns the relation between input features and corresponding output labels from the provided data. Once the model learns from the data, we have a trained model.  Once the model is trained, it can be used for inference. Inference is a process of getting a prediction by giving a data point. In this example, we input features of a cat or a dog, and the trained model predicts the output that is a cat or a dog label.  The types of machine learning models depend on whether we have a labeled output or not.  04:28 Nikita: Oh, there are different types of machine learning models? Hemant: In general, there are three types of machine learning approaches.  In supervised machine learning, labeled data is used to train the model. Model learns the relation between features and labels.  Unsupervised learning is generally used to understand relationships within a data set. Labels are not used or are not available.  Reinforcement learning uses algorithms that learn from outcomes to make decisions or choices.  05:06 Lois: Ok…supervised learning, unsupervised learning, and reinforcement learning. Where do we use each of these machine learning models? Hemant: Some of the popular applications of supervised machine learning are disease detection, weather forecasting, stock price prediction, spam detection, and credit scoring. For example, in disease detection, the patient data is input to a machine learning model, and machine learning model predicts if a patient is suffering from a disease or not.  For unsupervised machine learning, some of the most common real-time applications are to detect fraudulent transactions, customer segmentation, outlier detection, and targeted marketing campaigns. So for example, given the transaction data, we can look for patterns that lead to fraudulent transactions.  Most popular among reinforcement learning applications are automated robots, autonomous driving cars, and playing games.  06:12 Nikita: I want to get into how each type of machine learning works. Can we start with supervised learning? Hemant: Supervised learning is a machine learning model that learns from labeled data. The model learns the mapping between the input and the output.  As a house price predictor model, we input house size in square feet and model predicts the price of a house. Suppose we need to develop a machine learning model for detecting cancer, the input to the model would be the person's medical details, the output would be whether the tumor is malignant or not.  06:50 Lois: So, that mapping between the input and output is fundamental in supervised learning.  Hemant: Supervised learning is similar to a teacher teaching student. The model is trained with the past outcomes and it learns the relationship or mapping between the input and output.  In supervised machine learning model, the outputs can be either categorical or continuous. When the output is continuous, we use regression. And when the output is categorical, we use classification.  07:26 Lois: We want to keep this discussion at a high level, so we’re not going to get into regression and classification. But if you want to learn more about these concepts and look at some demonstrations, visit mylearn.oracle.com. Nikita: Yeah, look for the Oracle Cloud Infrastructure AI Foundations course and you’ll find a lot of resources that you can make use of.  07:51 The Oracle University Learning Community is an excellent place to collaborate and learn with Oracle experts and fellow learners. Grow your skills, inspire innovation, and celebrate your successes. All your activities, from liking a post to answering questions and sharing with others, will help you earn a valuable reputation, badges, and ranks to be recognized in the community. Visit mylearn.oracle.com to get started.  08:19 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Hemant? Hemant: Unsupervised machine learning is a type of machine learning where there are no labeled outputs. The algorithm learns the patterns and relationships in the data and groups similar data items. In unsupervised machine learning, the patterns in the data are explored explicitly without being told what to look for. For example, if you give a set of different-colored LEGO pieces to a child and ask to sort it, it may the LEGO pieces based on any patterns they observe. It could be based on same color or same size or same type. Similarly, in unsupervised learning, we group unlabeled data sets.  One more example could be-- say, imagine you have a basket of various fruits-- say, apples, bananas, and oranges-- and your task is to group these fruits based on their similarities. You observe that some fruits are round and red, while others are elongated and yellow. Without being told explicitly, you decide to group the round and red fruits together as one cluster and the elongated and yellow fruits as another cluster. There you go. You have just performed an unsupervised learning task.  09:42 Lois: Where is unsupervised machine learning used? Can you take us through some use cases? Hemant: The first use case of unsupervised machine learning is market segmentation. In market segmentation, one example is providing the purchasing details of an online shop to a clustering algorithm. Based on the items purchased and purchasing behavior, the clustering algorithm can identify customers based on the similarity between the products purchased. For example, customers with a particular age group who buy protein diet products can be shown an advertisement of sports-related products.  The second use case is on outlier analysis. One typical example for outlier analysis is to provide credit card purchase data for clustering. Fraudulent transactions can be detected by a bank by using outliers. In some transaction, amounts are too high or recurring. It signifies an outlier.  The third use case is recommendation systems. An example for recommendation systems is to provide users' movie viewing history as input to a clustering algorithm. It clusters users based on the type or rating of movies they have watched. The output helps to provide personalized movie recommendations to users. The same applies for music recommendations also.  11:14 Lois: And finally, Hemant, let’s talk about reinforcement learning. Hemant: Reinforcement learning is like teaching a dog new tricks. You reward it when it does something right, and over time, it learns to perform these actions to get more rewards. Reinforcement learning is a type of Machine Learning that enables an agent to learn from its interaction with the environment, while receiving feedback in the form of rewards or penalties without any labeled data.  Reinforcement learning is more prevalent in our daily lives than we might realize. The development of self-driving cars and autonomous drones rely heavily on reinforcement learning to make real time decisions based on sensor data, traffic conditions, and safety considerations.  Many video games, virtual reality experiences, and interactive entertainment use reinforcement learning to create intelligent and challenging computer-controlled opponents. The AI characters in games learn from player interactions and become more difficult to beat as the game progresses.  12:25 Nikita: Hemant, take us through some of the terminology that’s used with reinforcement learning. Hemant: Let us say we want to train a self-driving car to drive on a road and reach its destination. For this, it would need to learn how to steer the car based on what it sees in front through a camera. Car and its intelligence to steer on the road is called as an agent.  More formally, agent is a learner or decision maker that interacts with the environment, takes actions, and learns from the feedback received. Environment, in this case, is the road and its surroundings with which the car interacts. More formally, environment is the external system with which the agent interacts. It is the world or context in which the agent operates and receives feedback for its actions.  What we see through a camera in front of a car at a moment is a state. State is a representation of the current situation or configuration of the environment at a particular time. It contains the necessary information for the agent to make decisions. The actions in this example are to drive left, or right, or keep straight. Actions are a set of possible moves or decisions that the agent can take in a given state.  Actions have an impact on the environment and influence future states. After driving through the road many times, the car learns what action to take when it views a road through the camera. This learning is a policy. Formally, policy is a strategy or mapping that the agent uses to decide which action to take in a given state. It defines the agent's behavior and determines how it selects actions.  14:13 Lois: Ok. Say we’re talking about the training loop of reinforcement learning in the context of training a dog to learn tricks. We want it to pick up a ball, roll, sit… Hemant: Here the dog is an agent, and the place it receives training is the environment. While training the dog, you provide a positive reward signal if the dog picks it right and a warning or punishment if the dog does not pick up a trick. In due course, the dog gets trained by the positive rewards or negative punishments.  The same tactics are applied to train a machine in the reinforcement learning. For machines, the policy is the brain of our agent. It is a function that tells what actions to take when in a given state. The goal of reinforcement learning algorithm is to find a policy that will yield a lot of rewards for the agent if the agent follows that policy referred to as the optimal policy.  Through a process of learning from experiences and feedback, the agent becomes more proficient at making good decisions and accomplishing tasks. This process continues until eventually we end up with the optimal policy. The optimal policy is learned through training by using algorithms like Deep Q Learning or Q Learning.  15:40 Nikita: So through multiple training iterations, it gets better. That’s fantastic. Thanks, Hemant, for joining us today. We’ve learned so much from you. Lois: Remember, the course and certification are free, so if you’re interested, make sure you log in to mylearn.oracle.com and get going. Join us next week for another episode of the Oracle University Podcast. Until then, I’m Lois Houston… Nikita: And Nikita Abraham signing off! 16:09 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

30 Apr 202425min

You probably interact with artificial intelligence (AI) more than you realize. So, there’s never been a better time to start figuring out how it all works.   Join Lois Houston and Nikita Abraham as they decode the fundamentals of AI so that anyone, irrespective of their technical background, can leverage the benefits of AI and tap into its infinite potential.   Together with Senior Cloud Engineer Nick Commisso, they take you through key AI concepts, common AI tasks and domains, and the primary differences between AI, machine learning, and deep learning.   Oracle MyLearn: https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2023/127177   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Himanshu Raj, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 The world of artificial intelligence is vast and everchanging. And with all the buzz around it lately, we figured it was the perfect time to revisit our AI Made Easy series. Join us over the next few weeks as we chat about all things AI, helping you to discover its endless possibilities. Ready to dive in? Let’s go! 00:33 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:46 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Welcome to a new season of the Oracle University Podcast. I’m so excited about this season because we’re going to delve into the world of artificial intelligence. In upcoming episodes, we’ll talk about the fundamentals of artificial intelligence and machine learning. And we’ll discuss neural network architectures, generative AI and large language models, the OCI AI stack, and OCI AI services. 01:27 Nikita: So, if you’re an IT professional who wants to start learning about AI and ML or even if you’re a student who is familiar with OCI or similar cloud services, but have no prior exposure to this field, you’ll want to tune in to these episodes. Lois: That’s right, Niki. So, let’s get started. Today, we’ll talk about the basics of artificial intelligence with Senior Cloud Engineer Nick Commisso. Hi Nick! Thanks for joining us today. So, let’s start right at the beginning. What is artificial intelligence? 01:57 Nick: Well, the ability of machines to imitate the cognitive abilities and problem solving capabilities of human intelligence can be classified as artificial intelligence or AI.  02:08 Nikita: Now, when you say capabilities and abilities, what are you referring to? Nick: Human intelligence is the intellectual capability of humans that allows us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using a language and understand the nonverbal cues, such as facial recognition, tone variation, and body language.  You can handle objections in real time, even in a complex setting. You can plan for short and long-term situations or projects. And, of course, you can create music and art or invent something new like an original idea.  If you can replicate any of these human capabilities in machines, this is artificial general intelligence or AGI. So in other words, AGI can mimic human sensory and motor skills, performance, learning, and intelligence, and use these abilities to carry out complicated tasks without human intervention.  When we apply AGI to solve problems with specific and narrow objectives, we call it artificial intelligence or AI.  03:16 Lois: It seems like AI is everywhere, Nick. Can you give us some examples of where AI is used? Nick: AI is all around us, and you've probably interacted with AI, even if you didn't realize it. Some examples of AI can be viewing an image or an object and identifying if that is an apple or an orange. It could be examining an email and classifying it spam or not. It could be writing computer language code or predicting the price of an older car.  So let's get into some more specifics of AI tasks and the nature of related data. Machine learning, deep learning, and data science are all associated with AI, and it can be confusing to distinguish.  03:57 Nikita: Why do we need AI? Why’s it important?  Nick: AI is vital in today's world, and with the amount of data that's generated, it far exceeds the human ability to absorb, interpret, and actually make decisions based on that data. That's where AI comes in handy by enhancing the speed and effectiveness of human efforts.  So here are two major reasons why we need AI. Number one, we want to eliminate or reduce the amount of routine tasks, and businesses have a lot of routine tasks that need to be done in large numbers. So things like approving a credit card or a bank loan, processing an insurance claim, recommending products to customers are just some example of routine tasks that can be handled.  And second, we, as humans, need a smart friend who can create stories and poems, designs, create code and music, and have humor, just like us.  04:54 Lois: I’m onboard with getting help from a smart friend! There are different domains in AI, right, Nick?  Nick: We have language for language translation; vision, like image classification; speech, like text to speech; product recommendations that can help you cross-sell products; anomaly detection, like detecting fraudulent transactions; learning by reward, like self-driven cars. You have forecasting with weather forecasting. And, of course, generating content like image from text.  05:24 Lois: There are so many applications. Nick, can you tell us more about these commonly used AI domains like language, audio, speech, and vision? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, or extracting key phrases and so on.  Consider the example of translating text. There's many text translation tools where you simply type or paste your text into a given text box, choose your source and target language, and then click translate.  Now, let's look at the generative AI tasks. They are generative, which means the output text is generated by a model. Some examples are creating text like stories or poems, summarizing a text, answering questions, and so on. Let's take the example of ChatGPT, the most well-known generative chat bot. These bots can create responses from their training on large language models, and they continuously grow through machine learning.  06:31 Nikita: What can you tell us about using text as data? Nick: Text is inherently sequential, and text consists of sentences. Sentences can have multiple words, and those words need to be converted to numbers for it to be used to train language models. This is called tokenization. Now, the length of sentences can vary, and all the sentences lengths need to be made equal. This is done through padding.  Words can have similarities with other words, and sentences can also be similar to other sentences. The similarity can be measured through dot similarity or cosine similarity. We need a way to indicate that similar words or sentences may be close by. This is done through representation called embedding.  07:17 Nikita: And what about language AI models? Nick: Language AI models refer to artificial intelligence models that are specifically designed to understand, process, and generate natural language. These models have been trained on vast amounts of textual data that can perform various natural language processing or NLP tasks.  The task that needs to be performed decides the type of input and output. The deep learning model architectures that are typically used to train models that perform language tasks are recurrent neural networks, which processes data sequentially and stores hidden states, long short-term memory, which processes data sequentially that can retain the context better through the use of gates, and transformers, which processes data in parallel. It uses the concept of self-attention to better understand the context.  08:09 Lois: And then there’s speech-related AI, right? Nick: Speech-related AI tasks can be either audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech-to-text conversion or speaker recognition, voice conversion, and so on. Generative AI tasks are generative in nature, so the output audio is generated by a model. For example, you have music composition and speech synthesis.  Audio or speech is digitized as snapshots taken in time. The sample rate is the number of times in a second an audio sample is taken. Most digital audio have a sampling rate of 44.1 kilohertz, which is also the sampling rate for audio CDs.  Multiple samples need to be correlated to make sense of the data. For example, listening to a song for a fraction of a second, you won't be able to infer much about the song, and you'll probably need to listen to it a little bit longer.  Audio and speech AI models are designed to process and understand audio data, including spoken language. These deep-learning model architectures are used to train models that perform language with tasks-- recurrent neural networks, long short-term memory, transformers, variational autoencoders, waveform models, and Siamese networks. All of the models take into consideration the sequential nature of audio.  09:42 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification.  Visit mylearn.oracle.com to get started.  10:10 Nikita: Welcome back! Now that we’ve covered language and speech-related tasks, let’s move on to vision-related tasks. Nick: Vision-related AI tasks could be image related or generative AI. Image-related AI tasks will use an image as an input, and the output depends on the task. Some examples are classifying images, identifying objects in an image, and so on. Facial recognition is one of the most popular image-related tasks that is often used for surveillance and tracking of people in real time, and it's used in a lot of different fields, including security, biometrics, law enforcement, and social media.  For generative AI tasks, the output image is generated by a model. For example, creating an image from a contextual description, generating images of a specific style or a high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of an object, machine components, buildings, medication, people, and even more.  11:14 Lois: So, then, here again I need to ask, how do images work as data? Nick: Images consist of pixels, and pixels can be either grayscale or color. And we can't really make out what an image is just by looking at one pixel.  The task that needs to be performed decides the type of input needed and the output produced. Various architectures have evolved to handle this wide variety of tasks and data. These deep-learning model architectures are typically used to train models that perform vision tasks-- convolutional neural networks, which detects patterns in images; learning hierarchical representations of visual features; YOLO, which is You Only Look Once, processes the image and detects objects within the image; and then you have generative adversarial networks, which generates real-looking images.  12:04 Nikita: Nick, earlier you mentioned other AI tasks like anomaly detection, recommendations, and forecasting. Could you tell us more about them? Nick: Anomaly detection. This is time-series data, which is required for anomaly detection, and it can be a single or multivariate for fraud detection, machine failure, etc.  Recommendations. You can recommend products using data of similar products or users. For recommendations, data of similar products or similar users is required.  Forecasting. Time-series data is required for forecasting and can be used for things like weather forecasting and predicting the stock price.  12:43 Lois: Nick, help me understand the difference between artificial intelligence, machine learning, and deep learning. Let’s start with AI.  Nick: Imagine a self-driving car that can make decisions like a human driver, such as navigating traffic or detecting pedestrians and making safe lane changes. AI refers to the broader concept of creating machines or systems that can perform tasks that typically require human intelligence. Next, we have machine learning or ML. Visualize a spam email filter that learns to identify and move spam emails to the spam folder, and that's based on the user's interaction and email content. Now, ML is a subset of AI that focuses on the development of algorithms that enable machines to learn from and make predictions or decisions based on data.  To understand what an algorithm is in the context of machine learning, it refers to a specific set of rules, mathematical equations, or procedures that the machine learning model follows to learn from data and make predictions on. And finally, we have deep learning or DL. Think of an image recognition software that can identify specific objects or animals within images, such as recognizing cats in photos on the internet. DL is a subfield of ML that uses neural networks with many layers, deep neural networks, to learn and make sense of complex patterns in data.  14:12 Nikita: Are there different types of machine learning? Nick: There are several types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning where the algorithm learns from labeled data, making predictions or classifications. Unsupervised learning is an algorithm that discovers patterns and structures in unlabeled data, such as clustering or dimensionality reduction. And then, you have reinforcement learning, where agents learn to make predictions and decisions by interacting with an environment and receiving rewards or punishments.  14:47 Lois: Can we do a deep dive into each of these types you just mentioned? We can start with the supervised machine learning algorithm. Nick: Let's take an example of how a credit card company would approve a credit card. Once the application and documents are submitted, a verification is done, followed by a credit score check and another 10 to 15 days for approval. And how is this done? Sometimes, purely manually or by using a rules engine where you can build rules, give new data, get a decision.  The drawbacks are slow. You need skilled people to build and update rules, and the rules keep changing. The good thing is that the businesses had a lot of insight as to how the decisions were made. Can we build rules by looking at the past data?  We all learn by examples. Past data is nothing but a set of examples. Maybe reviewing past credit card approval history can help. Through a process of training, a model can be built that will have a specific intelligence to do a specific task. The heart of training a model is an algorithm that incrementally updates the model by looking at the data samples one by one.  And once it's built, the model can be used to predict an outcome on a new data. We can train the algorithm with credit card approval history to decide whether to approve a new credit card. And this is what we call supervised machine learning. It's learning from labeled data.  16:13 Lois: Ok, I see. What about the unsupervised machine learning algorithm? Nick: Data does not have a specific outcome or a label as we know it. And sometimes, we want to discover trends that the data has for potential insights. Similar data can be grouped into clusters. For example, retail marketing and sales, a retail company may collect information like household size, income, location, and occupation so that the suitable clusters could be identified, like a small family or a high spender and so on. And that data can be used for marketing and sales purposes.  Regulating streaming services. A streaming service may collect information like viewing sessions, minutes per session, number of unique shows watched, and so on. That can be used to regulate streaming services. Let's look at another example. We all know that fruits and vegetables have different nutritional elements. But do we know which of those fruits and vegetables are similar nutritionally?  For that, we'll try to cluster fruits and vegetables' nutritional data and try to get some insights into it. This will help us include nutritionally different fruits and vegetables into our daily diets. Exploring patterns and data and grouping similar data into clusters drives unsupervised machine learning.  17:34 Nikita: And then finally, we come to the reinforcement learning algorithm.  Nick: How do we learn to play a game, say, chess? We'll make a move or a decision, check to see if it's the right move or feedback, and we'll keep the outcomes in your memory for the next step you take, which is learning. Reinforcement learning is a machine learning approach where a computer program learns to make decisions by trying different actions and receiving feedback. It teaches agents how to solve tasks by trial and error.  This approach is used in autonomous car driving and robots as well.  18:06 Lois: We keep coming across the term “deep learning.” You’ve spoken a bit about it a few times in this episode, but what is deep learning, really? How is it related to machine learning? Nick: Deep learning is all about extracting features and rules from data. Can we identify if an image is a cat or a dog by looking at just one pixel? Can we write rules to identify a cat or a dog in an image? Can the features and rules be extracted from the raw data, in this case, pixels?  Deep learning is really useful in this situation. It's a special kind of machine learning that trains super smart computer networks with lots of layers. And these networks can learn things all by themselves from pictures, like figuring out if a picture is a cat or a dog.  18:49 Lois: I know we’re going to be covering this in detail in an upcoming episode, but before we let you go, can you briefly tell us about generative AI? Nick: Generative AI, a subset of machine learning, creates diverse content like text, audio, images, and more. These models, often powered by neural networks, learn patterns from existing data to craft fresh and creative output. For instance, ChatGPT generates text-based responses by understanding patterns in text data that it's been trained on. Generative AI plays a vital role in various AI tasks requiring content creation and innovation.  19:28 Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the Oracle Cloud Infrastructure AI Foundations course. As you complete the course, you’ll find skill checks that you can attempt to solidify your learning.  Lois: And remember, the AI Foundations course on MyLearn also prepares you for the Oracle Cloud Infrastructure 2023 AI Foundations Associate certification. Both the course and the certification are free, so there’s really no reason NOT to take the leap into AI, right Niki? Nikita: That’s right, Lois! Lois: In our next episode, we will look at the fundamentals of machine learning. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 20:13 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

23 Apr 202420min

Visual Builder Studio requires its data sources to connect to the webpage it produces using REST calls. Therefore, the data source has to provide a REST interface. A simple, easy, secure, and free way to do that is with Oracle REST Data Services (ORDS).   In this episode, hosts Lois Houston and Nikita Abraham chat with Senior Principal OCI Instructor Joe Greenwald about what ORDS can do, how to easily set it up, how to work with it, and how to use it within Visual Builder Studio.   Develop Fusion Applications Using Visual Builder Studio: https://mylearn.oracle.com/ou/course/develop-fusion-applications-using-visual-builder-studio/122614/   Build Visual Applications Using Visual Builder Studio: https://mylearn.oracle.com/ou/course/build-visual-applications-using-oracle-visual-builder-studio/110035/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26   Nikita: Hello and welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs.   Lois: Hi there! In our last episode, we took a look at model-based development tools, their start as CASE tools, what they morphed into, and how they’re currently used in Oracle software development. We’re wrapping up the season with this episode today, which will be about how to access Oracle database data through a REST interface created and managed by Oracle REST Data Services, or ORDS, and how to access this data in Visual Builder Studio.   01:03 Nikita: Being able to access Oracle database data through a REST interface over the web is highly useful, but sometimes it can be complicated to create that interface in a programming language. Joe Greenwald, our Senior OCI Learning Solutions Architect and Principal Instructor is back with us one last time this season to tell us more about ORDS, and how it makes it much simpler and easier for us to REST-enable our database for use in tools like Visual Builder Studio. Hi Joe! Tell us a little about what Visual Builder Studio is and why we must REST-enable our data for VBS to be able to use it.   01:40 Joe: Hi Niki, hi Lois! Ok, so, Visual Builder Studio is Oracle’s low-code software development and project asset management product for creating graphical webpage front-ends for web applications. It’s the tool of choice for designing, building, and implementing all of Oracle Fusion Cloud Applications and is being used by literally tens of thousands of engineers at Oracle now to bring the next generation of Fusion Applications to our customers and the market. It’s based on standards like HTML5, CSS3, and JavaScript. It’s highly performant and combined with the Redwood graphical design system and components that we talked about previously, delivers a world-class experience for users. One thing about Visual Builder Studio though: it only works with data sources that have a REST interface. This is unusual. I like to think I’ve worked with every software development tool that Oracle’s created since I joined Oracle in 1992, including some unreleased ones, and all of them allowed you to talk to the database directly. This is the first time that we’ve released a tool that I know of where we don’t do that. Now at first, I was a little put off and wondered how’s it going to do this and how much work I would have to do to create a REST interface for some simple tables in the Oracle database. Like, here’s one more thing I must do just to create a page that displays data from the database. As it turns out, it’s a wise design decision on the part of the designers. It simplifies the data access parts of Visual Builder Studio and makes the data access model common across the different data sources. And, thanks to ORDS, REST-enabling data in Oracle database couldn’t be easier! 03:13 Lois: That’s cool. We don’t want to focus too much on Visual Builder Studio today. We have free courses that teach you how to create service connections to REST services to access the data and all of that. What we actually want to talk with you about is working with Oracle REST Data Service. How easy is it to work with Oracle REST Data Service to add REST support, what we call REST-enable your Oracle Database, and why it is important?  Nikita: Yeah, I could use a bit of a refresher on REST myself. Could you describe what REST is, how it works for both the client and server, and what ORDS is doing for us? 03:50 Joe: Sure. So, REST is a way to make a request to a server for a resource using the HTTP web protocol from a client, like your browser, to a web server, which hands off the request to code that handles the request and sends the response back to your client/browser, which then uses it, displays or whatever. So, you can see we have two parts. We have the client, which makes the request, and the server, which handles the request and figures out what the response should be (static, dynamic, or a combination of both) and sends that back to the client. For example, a visual application built with Visual Builder Studio acts as a client making the request, just as your browser makes a request. It’s really just a web app built with HTML5, CSS3, and JavaScript, and the JavaScript makes a request to the server on your behalf. Let’s say you wish to access your student record within Oracle University. And now, this is a contrived example and it won’t actually work, but it’s good for illustrative purposes.  Oracle University, let’s say, publishes the URL for your student data as something like https://oracle.com/oracleuniversity/student/{studentnumber} you put in some kind of number, like the number 23, and if you enter that into your browser address bar and press Enter, then your browser, on your behalf, sends a GET request—what we call an HTTP GET operation—to the web server. When the web server receives the request, it will somehow read the record for student 23, format a response, and send the response back to the client. 05:16 Joe: That’s a GET or a READ request. Now, what if you are creating a new student? Well, you fill out a form on the webpage and you click the Submit button. And it sends a POST request, which tells the server to create a new record in its storage mechanism, most likely a database of some form. If you do an update, you change certain fields on the webpage and click the Submit button and this time, an update request is made. If you wanted to delete the record, you’d find the record you want to delete and press the Submit button, and this time a delete request is made. This is the general idea, though there are different ways to do creates and updates that are really irrelevant here. Those requests to the server I mentioned are called HTTP operations and there are several of them. But the four most popular are GET to retrieve data, POST to create a new record on the server, PUT to update a record, and DELETE to remove a record.  On the client side, we just need to specify where the record is that we want to retrieve—that’s the oracle.com/oracleuniversity/student part of the URL and an identifying value, which makes it unique. So, when I do a GET request on customer or student 23, I’m going to get back a representation of the student data that exists in the student database for a student with ID 23. There should not be more than one of these or that would indicate an error. The response typically comes back in a format of a key:value pair called JavaScript Object Notation (JSON), but it could also be in a Text format, HTML, Excel, PDF, or whatever the server implements and is requested.  06:42 Nikita: OK great! That’s on the client side making the request. But what’s happening on the server side? Do I need to worry about that if I’m the client? Joe: No, that’s the great part. As a client, I don’t know, and frankly I’d rather not know what the server’s doing or how it does it. I don’t want to be dependent on the server implementation at all. I simply want to make a request and the server handles the request and sends a response. Now, just a word about what’s on the server. Some data on the server is static like a PDF file or an image or an audio file, for example, and sometimes you’ll see that in the URL the file type as an extension, like .pdf, and you get back a PDF file that your browser displays or that you can download to your machine. But with dynamic data, like student data coming out of a database based on the student number, a query is made against a database. The database responds with the data, and that’s formatted into some type of data format—typically JSON—and sent back to the client, which then does something with it, like displaying it on a webpage. So, as we can see, the client is fairly simple in the sense that it makes a request, receives the data response, and displays it or does something with it. And that’s one of the reasons why the choice to use REST and only REST in Visual Builder Studio is such a wise one. 07:54 Joe: Regardless of the different data sources or the different server implementations or how the data is stored on the server, or any of that, Visual Builder Studio doesn’t know and doesn’t care. What it sees is the REST request it sends and the response it gets back and then it deals with the response data regardless of how it’s implemented on the server. I mentioned the server sends back a representation of the resource, in this case, for example, the student record. That’s really where the abbreviation REST comes from: REpresentational State Transformation, which is a long way of saying, bring me back a representation of the resource—the thing—that I’m requesting. Now, of course, the server is a little more complex. On the server side, we would need software that is going to take the request from the web server using some programming language like Java, C#, C++, Python, or maybe even JavaScript in a Node.js application. You have a program that receives a request from the web server, executes the request (typically by connecting to the database if it’s a database call), makes the request, receives a data response from the database, formats that into some form, and passes it back to the web server, which then sends it back to the client that requested it. 09:01 Lois: Ok… I think I see. I’m guessing that ORDS gets involved somehow between the client and the server. Joe: Yes, exactly. We can see that the implementation on the server side is where the complexity is. For example, if I implement a student management service in Java, I have to write a bunch of Java code, a lot of which is boilerplate, housekeeping, boring code. For simple database access, it’s tedious to have to do this over and over, and if the database changes, it can be even more tedious to maintain that code to handle simple to moderately complex requests. Writing and maintaining software code to just read and write data from the database to pass to a client for a web request is cool the very first time you do it and then gets boring very quickly and it’s prone to errors because it’s so manual. So, it would be nice if we had a piece of software that could handle the tedious, boring, manual bits of this service. It would receive the request that our client, the browser or Visual Builder Studio for example, is sending, take that request, execute the request against the database for us, receive the response from the database, and then format it for us and send it back to us, without a developer having to write custom code on the server side. And that is what Oracle REST Data Services (ORDS) does. 10:13 Joe: ORDS contains a lightweight web server based on the Jetty web server that receives the request from the client, like a browser or Visual Builder Studio or whatever, in the form of a URL, parses the request, generates a query or an update, or an insert or delete, depending on the nature of the HTTP operation sent or requested, and sends it to the database on our behalf. The database executes the request from ORDS, sends back a response to ORDS, and ORDS formats the response for us in the JSON and sends it back to our client. In nutshell, that’s it. 10:45 Lois: So ORDS does all that? And it’s free? How does it work? Uhm, remember I’m not as technical as you are. Joe: Of course. ORDS is free. It’s a lightweight, highly performant Java app that can run in many different modes, from stand-alone on a server to embedded in an application server like WebLogic, to running in the Oracle Cloud with the Oracle Autonomous Database (ADB). When you REST-enable your tables, your web requests are intercepted by ORDS running in ADB. It’s optimized for the purpose of handling web requests, connecting to the Oracle database, and sending back formatted responses as JSON. It can also handle more complex requests as well in the form of queries with special parameters.  So, you can see what ORDS does for us. It handles the request coming from the client, which could be a browser or Visual Builder Studio or APEX or whatever client—pretty much any client today can make an HTTP call—it handles the call, parses the request, makes the request to the server on our behalf, and of course security is built-in and all of that, and so we don’t get to data we’re not supposed to see. It receives a response from the database, formats it into the JSON key:value pair format, and sends it back to our client. 12:00 Are you planning to become an Oracle Certified Professional this year? Whether you're a seasoned IT pro or just starting your career, getting certified can give you a significant boost. And don't worry, we've got your back. Join us at one of our cert prep live events in the Oracle University Learning Community. You'll get insider tips from seasoned experts and learn from other professionals' experiences. Plus, once you've earned your certification, you'll become part of our exclusive forum for Oracle-certified users. So, what are you waiting for? Head over to mylearn.oracle.com and create an account to jump-start your journey towards certification today! 12:43 Nikita: Welcome back. So, Joe, then the next question is, what do we do to REST-enable our database? Does that only work for ADB? Joe: This can be done in a couple of different ways. It can be done implicitly, called AutoREST, or explicitly. AutoREST is very convenient. In the case of an ADB database, you log in as the user who owns the structures, select your tables, views, packages, procedures, or functions that you want to REST-enable. Choose REST and then Enable from the menu for the table, view, stored package, procedure, or function and a URL is generated using your POST, GET, PUT, and DELETE for the standard database create, retrieve, update, delete operations. And it’s not just for ADB. You can do this in SQL Developer Desktop as well. Then, when you invoke the URL for the service, if you include just the name of the resource, like students, you get the entire collection back. If you add an ID at the end of the URL, like student/23, you get back the data for that specific student back, or whatever the structure is. You can add more complex filter parameters as well. And that’s it! Very easy. And, of course, you can apply appropriate security and ORDS enforces it.  But you also can create custom code to handle more complex requests.  13:53 Lois: Joe, what if there’s custom logic or processing that you want to do when the REST call comes in and you need to write custom code to handle it? Joe: Remember, I said on the server side, we use custom code to retrieve data as well as apply business rules, validations, edits, whatever needs to be done to appropriately handle the REST call. So it’s a great question, Lois. When using ORDS, you can write a REST service handler in PL/SQL and SQL, just like if you were writing a stored procedure or a function or a package in the database, which is exactly what you’re doing. ORDS exposes your PL/SQL code wrapped in a REST interface with, of course, the necessary security. And since it’s PL/SQL, it runs in the database, so it’s highly performant, fast, and uses code you’re likely already familiar with or maybe already have. Your REST service handler can call existing PL/SQL packages, procedures, and functions. For example, if you created packages with stored procedures and functions that wrap access to your database tables and views, you can REST-enable those stored procedures, functions, and packages, and call them over the web. And maintain the package access you already created. I do want to point out that the recommended way to access your tables and views is through packages, stored procedures, and functions. While you can expose your tables and views directly to REST, should you really do that? In practice, it’s generally not a recommended way to do it. Do you want to expose your data in tables and views directly through a REST interface? Ideally, no, access should be through a PL/SQL wrapper, same as it’s—hopefully—done today for your client-server applications. 15:26 Nikita: I understand it’s easy to generate a simple REST interface for tables and so on to do basic create, retrieve, update, and delete operations. But what’s required to create custom code to handle more complex business operations?  Joe: The process to create your own custom handlers is a little bit more involved as you would expect. It uses your skills as a PL/SQL programmer, while hiding the details of the REST implementation to let you focus on the logic and processing. Mechanically, you’d begin by creating a module that has a URL associated with it. So, for example, you would create a URL like https://oracle.com/oracleuniversity/studentregistry. Then, within that module, you create a template that names the specific resource—or thing—that you want to work with. For example, student, or course, or registration. 16:15 Joe: Then you create the handler for it. You have a handler to do the read, another handler for the insert, another handler for an update, another handler for a delete, and even possibly multiple handlers for more complex APIs based on your needs and the parameters being passed in.  You can create complex URLs with multiple parameters for passing needed information into the PL/SQL procedure, which is going to do the actual programming work for you. There are predefined implicit variables about the message itself that you can use, as well as all the parameters from the URL itself. Now, this is all done in a nice developer interface on the web if you’re using SQL Developer Web with ADB or in SQL Developer for the desktop. Either one can do this because under the covers, ORDS is generating and executing the PL/SQL calls necessary to create and expose your web services. It’s very easy to work with and test immediately. 17:06 Lois: Joe, how much REST knowledge do I need to use ORDS properly to create REST services? Joe: Well, you should have some basic knowledge of REST, HTTP operations, request and response messages, and JSON, since this is the data format ORDS produces. The developer interface is really not designed for somebody who knows nothing about REST at all; it’s not designed to take them step-by-step through everything that needs to be done. It’s not wizard-based. Rather, it’s an efficient, minimal interface that can be used quickly and easily by someone who has at least some experience building REST services. But, if you have a little knowledge and you understand how REST works and how a REST interface is used and you understand PL/SQL and SQL, you could do quite a lot with only minimal knowledge. It’s easy to get started and it’s fun to see your data start appearing in webpages formatted for you, with very little or even no code at all as in the case of AutoREST enabling. And ORDS is free and comes as part of the database in ADB as SQL Developer Web and SQL Developer Desktop, both of which are free as well. And SQL Developer Web and SQL Developer Desktop both have a data modeler built into them so you can model your database tables, columns, and keys, and generate and execute the code necessary to create the structures immediately, and they can create graphical models of your database to aid in understanding and communication. Now, while this is not required, modeling your database structures before you build them is most definitely a best practice. 18:29 Nikita: Ok, so now that I have my REST-enabled database tables and all, how do I use them in VBS Designer? Joe: In Visual Builder Studio Designer, you define a service connection by its endpoint and paste the URL for the REST-enabled resource into the wizard, and it generates everything for you by introspecting the REST service. You can test it, see the data shape of the response, and see data returned. You access your REST-enabled data from your database from Visual Builder Studio Designer and use it to populate lists, tables, and forms using the quick start wizards built in. I’ll also mention that ORDS provides other capabilities in addition to handling REST calls for the database tables and views. It also exposes over 500 different endpoints for managing your Oracle Database, things like Pluggable Database Management (PDBs), Data Pump, Data Dictionary, Performance, and Monitoring. It’s very easy to use and get started with. A great place to start is to create a free, autonomous database in Oracle Cloud, start it up, and then access the database actions. You can start creating tables, columns, and keys, and loading data, or you can load your own scripts, if you’ve got them, to produce the tables and columns and load them. You can upload the script and run it and it will create your tables and other needed structures. You can then REST-enable them by selecting simple menu options. It’s a lot of fun and easy to get started with. 19:47 Lois: So much good stuff today. Thank you, Joe, for being with us today and in the past few weeks and sharing your knowledge with us. Nikita: Yeah, it’s been so nice to have you around. Joe: Thank you both! It’s been great being here with you. 19:59 Lois: And remember, our Visual Builder courses, Develop Visual Applications with VBS and Develop Fusion Apps with VBS, both show you how to work with a third-party REST service. And our data modeling and design course teaches the fundamentals of data modeling. You can access all these of courses, for free, on mylearn.oracle.com. Join us next week for another episode of the Oracle University Podcast. Until then, I’m Lois Houston… Nikita: And Nikita Abraham signing off! 20:30 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

16 Apr 202420min

Computer Aided Software Engineering (CASE) tools, which helped make the analysis, design, and implementation phases of software development better, faster, and cheaper, fell out of favor in the mid-'90s. Yet much of what they have to offer remains and is in active use within different Oracle tools.   Listen to Lois Houston and Nikita Abraham interview Senior Principal OCI Instructor Joe Greenwald about the origins of CASE tools and model-based development, as well as how they evolved into their current forms.   Develop Fusion Applications Using Visual Builder Studio: https://mylearn.oracle.com/ou/course/develop-fusion-applications-using-visual-builder-studio/122614/   Build Visual Applications Using Visual Builder Studio: https://mylearn.oracle.com/ou/course/build-visual-applications-using-oracle-visual-builder-studio/110035/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started. 00:26   Nikita: Hello and welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and joining me is Lois Houston, Director of Innovation Programs.   Lois: Hi there! In our last episode, we looked at Oracle’s Redwood design system and how it helps create world-class apps and user experiences. Today, Joe Greenwald, our Senior Principal OCI Instructor, is back on our podcast. We’re going to focus on where model-based development tools came from: their start as CASE tools, how they morphed into today’s model-based development tools, and how these tools are currently used in Oracle software development to make developers’ lives better.   01:08 Nikita: That’s right. It’s funny how things that fell out of favor years ago come back and are used to support our app development efforts today. Hi Joe!   Joe: Haha! Hi Niki. Hi Lois.
 01:18 Lois: Joe, how did you get started with CASE tools?    Joe: I was first introduced to computer-aided software engineering tools, called CASE tools, in the late 1980s when I began working with them at Arthur Young consulting and then Knowledgeware corporation in Atlanta, helping customers improve and even automate their software development efforts using structured analysis and design techniques, which were popular and in high use at that time. But it was a pain to have to draw diagrams by hand, redraw them as specifications changed, and then try to maintain them to represent the changes in understanding what we were getting from our analysis and design phase work. CASE tools were used to help us draw the pictures as well as enforce rules and provide a repository so we could share what we were creating with other developers. I was immediately attracted to the idea of using diagrams and graphical images to represent requirements for computer systems.  02:08 Lois: Yeah, you’re like me. You’re a visual person. Joe: Yes, exactly. So, the idea that I could draw a picture and a computer could turn that into executable code was fascinating to me. Pictures helped us understand what the analysts told us the users wanted, and helped us communicate amongst the teams, and they also helped us validate our understanding with our users. This was a critical aspect because there was a fundamental cognitive disconnect between what the users told the analysts they needed, what the analysts told us the users needed, and what we understood was needed, and what the user actually wanted. There’s a famous cartoon, you can probably find this on the web, that shows what the users wanted, what was delivered, and then all the iterations that the different teams go through trying to represent the simple original request.   I started using entity relationship diagrams, data flow diagrams, and structure charts to support the structured analysis, design, and information engineering methods that we were using at the time for our clients. Used correctly, these were powerful tools that resulted in higher quality systems because it forced us to answer questions earlier on and not wait until later in the project life cycle, when it’s more expensive and difficult to make changes. 03:16 Nikita: So, the idea was to try to get it wrong sooner. Joe: That’s right, Niki. We wanted to get our analysis and designs in front of the customer as soon as possible to find out what was wrong with our models and then change the code as early in the life cycle as possible where it was both easier and, more importantly, cheaper to make changes before solidifying it in code.   Of course, the key words here are “used correctly,” right? I saw the tools misused many times by those who weren’t trained properly or, more typically, by those whose software development methodology, if there even was one, didn’t use the tools properly—and of course the tools took the blame. CASE tools at the time held a lot of promise, but one could say vendors were overpromising and under delivering, although I did have a number of clients who were successful with them and could get useful support for their software development life cycle from the use of the tools. Since then, I’ve been very interested in using tools to make it easier for us to build software.   04:09 Nikita: So, let me ask you Joe, what is your definition of a CASE tool?
 Joe: I’m glad you asked, Niki, because I think many people have many different definitions. I’m precise about it, and maybe even a bit pedantic with the definition. The definition I use for a CASE tool comprises four things. One, it uses graphics, graphical symbols, and diagrams to represent requirements and business rules for the application. Two, there is a repository, either private, or shared, or both, of models, definitions, objects, requirements, rules, diagrams, and other assets that can be shared, reused, and almost more importantly, tracked. Three, there’s a rule-base that prevents you from drawing things that can’t be implemented. For example, Visio was widely regarded as a CASE tool, but it really wasn’t because it had no rules behind it. You could wire together anything you wanted, but that didn’t mean it could be built.  Fourth, it generates useful code, and it should do two-way engineering, where code, typically code changed outside the model, can be reverse engineered back into the model and apply updates to the model, and to keep the model and the source code in synchronization.   05:13 Joe: I came up with a good slogan for CASE tools years ago: a good CASE tool should automate the tedious, manual portions of software development. I’d add that one also needs to be smarter than the tools they’re using. Which reminds me, interestingly enough, of clients who would pick up CASE tools, thinking that they would make their software development life cycle shorter. But if they weren’t already building models for analysis or design, then automating the building of things that they weren’t building already was not going to save them time and effort.  And some people adopted CASE tools because they were told to or worse, forced to, or they read an article on an airplane, or had a Eureka moment, and they would try to get their entire software development staff to use this new tool, overnight literally, in some cases. Absolutely sheer madness!  Tools like this need to be brought into the enterprise in a slow, measured fashion with a pilot project and build upon small successes until people start demanding to use the tools in their own projects once they see the value. And each group, each team would use the CASE tool differently and to a different degree. One size most definitely does not fit all and identifying what the teams’ needs are and how the tool can automate and support those needs is an important aspect of adopting a CASE tool. It’s funny, almost everyone would agree there’s value in creating models and, eventually, generating code from them to get better systems and it should be faster and cheaper, etc. But CASE tools never really penetrated the market more than maybe about 18 to 25%, tops.   06:39 Lois: Huh, why? Why do you think CASE tools were not widely accepted and used?   Joe: Well, I don’t think it was an issue with the tools so much as it was with a company’s software development life cycle, and the culture and politics in the company. And I imagine you’re shocked to hear that.  Ideally, switching to or adopting automated tools like CASE tools would reduce development time and costs, and improve quality. So it should increase reusability too. But increasing the reusability of code elements and software assets is difficult and requires discipline, commitment, and investment. Also, there can be a significant amount of training required to teach developers, analysts, project managers, and senior managers how to deal with these different forms of life cycles and artifacts: how they get created, how to manage them, and how to use them. When you have project managers or senior managers asking where’s the code and you try telling them, “Well, it’s gonna take a little while. We’re building models and will press the button to generate the code.” That’s tough. And that’s also another myth. It was never a matter of build all the models, press the button, generate all the code, and be done. It’s a very iterative process. 07:40 Joe: I’ve also found that developers find it very psychologically reinforcing to type code into the keyboard, see it appear on the screen, see it execute, and models were not quite as satisfying in the same way. There was kind of a disconnect. And are still not today. Coders like to code.   So using CASE tools and the discipline that went along with them often created issues for customers because it could shine a bright light on the, well let’s say, less positive aspects of their existing software development process. And what was seen often wasn’t pretty. I had several clients who stopped using CASE tools because it made their poor development process highly visible and harder to ignore. It was actually easier for them to abandon the CASE tools and the benefits of CASE tools than to change their internal processes and culture. CASE tools require discipline, planning, preparation, and thoughtful approaches, and some places just couldn’t or wouldn’t do that. Now, for those who did have discipline and good software development practices, CASE tools helped them quite a bit—by creating documentation and automating the niggly little manual tasks they were spending a lot of time on.   08:43 Nikita: You’ve mentioned in the past that CASE tools are still around today, but we don’t call them that. Have they morphed into something else? And if so, what?   Joe: Ah, so the term Computer Aided Software Engineering morphed into something more acceptable in the ‘90s as vendors overpromised and under-delivered, because many people still saw value and do today see value in creating models to help with understanding, and even automating some aspects of software code development.  The term model-based development arose with the idea that you could build small models of what you want to develop and then use that to guide and help with manual code development. And frankly just not using the word CASE was a benefit. “Oh we’re not doing CASE tools, but we’ll still build pictures and do stuff.” So, it could be automated and generate useful code as well as documentation. And this was both easy to use and easier to manage, and I think the industry and the tools themselves were maturing. 09:35 Joe: So, model-based development took off and the idea of building a model to represent your understanding of the system became popular. And it was funny because people were saying that these were not CASE tools, this was something different, oh for sure, when of course it was pretty much the same thing: rule-based graphical modeling with a repository that created and read code—just named differently. And as I go through this, it reminds me of an interesting anecdote that’s given about US President Abraham Lincoln. He once asked someone, “If you call a dog’s tail a leg, how many legs does a dog have?” Now, while you’re thinking about that, I’ll go ahead and give you the correct answer. It’s four. You can call a dog’s tail anything you want, but it still has four legs. You can call your tools whatever you want, but you still have the idea of building graphical representations of requirements based on rules, and generating code and engineering in both directions. 10:29 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from cloud computing, database, and security to artificial intelligence and machine learning, all free to subscribers. So, what are you waiting for? Pick a topic, leverage the Oracle University Learning Community to ask questions, and then sit for your certification. Visit mylearn.oracle.com to get started. 10:58 Nikita: Welcome back! Joe, how did you come to Oracle and its CASE tools?   Joe: I joined Oracle in 1992 teaching the Oracle CASE tool Designer. It was focused on structured analysis and design, and could generate database Data Definition Language (DDL) for creating databases. And it was quite good at it and could reverse engineer databases as well. And it could generate Oracle Forms and Reports – character mode at first, and then GUI. But it was in the early days of the tool and there was definitely room for improvement, or as we would say opportunities for enhancement, and it could be hard to learn and work with. It didn’t do round-trip engineering of reading Oracle Forms code and updating the Designer models, though some of that came later. So now you had an issue where you could generate an application as a starting point, but then you had to go in and modify the code, and the code would get updated, but the models wouldn’t get updated and so little by little they’d go out of sync with the code, and it just became a big mess. But a lot of people saw that you could develop parts of the application and data definition in models and save time, and that led to what we call model-based development, where we use models for some aspects but not all. We use models where it makes sense and hand code where we need to code.    12:04 Lois: Right, so the two can coexist. Joe, how have model-based development tools been used at Oracle? Are they still in use?   Joe: Absolutely! And I’ll start with my favorite CASE tool at Oracle, uhm excuse me, model-based development tool. Oracle SOA Suite is my idea of a what a model-based development tool should be. We create graphical diagrams to represent the flow of messages and message processing in web services—both SOAP and REST interfaces—with logic handled by other diagrammers and models. We have models for logic, human interaction, and rules processing. All this is captured in XML metadata and displayed as nice, colored diagrams that are converted to source code once deployed to the server. The reason I like it so much is Oracle SOA Suite addressed a fundamental problem and weakness in using modeling tools that generated code. It doesn’t let the developer touch the generated code. I worked with many different CASE tools over the years, and they all suffered from a fundamental flaw. Analysts and developers would create the models, generate the code, eventually put it into production, and then, if there was a bug in the code, the developer would fix the code rather than change the model. For example, if a bug was found at 10:30 at night, people would get dragged out of bed to come down and fix things. What they should have done is update the model and then generate the new code. But late at night or in a crunch, who’s going to do that, right? They would fix the code and say they’d go back and update the model tomorrow. But as we know, tomorrow never comes, and so little by little, the model goes out of synchronization with the actual source code, and eventually people just stopped doing models. 13:33 Joe: And this just happened more and more until the use of CASE tools started diminishing—why would I build a model and have to maintain it to just maintain the code? Why do two separate things? Time is too valuable. So, the problem of creating models and generating code, and then maintaining the code and not the model was a problem in the industry. And I think it certainly hurt the adoption and progress of CASE tool adoption. This is one of the reasons why Oracle SOA Suite is my favorite CASE tool…because you never have access to the actual generated code. You are forced to change the model to get the new code deployed. Period. Problem solved. Well, SOA Suite does allow post- deployment changes, of course, and that can introduce consistency issues and while they’re easier to handle, we still have them! So even there, there’s an issue.   14:15 Nikita: How and where are modeling tools used in current Oracle software development applications?   Joe: While the use of CASE tools and even the name CASE fell out of favor in the early to mid-90s, the idea of using graphical diagrams to capture requirements and generate useful code does live on through to today. If you know what to look for, you can see elements of model-based design throughout all the Oracle tools. Oracle tools successfully use diagrams, rules, and code generation, but only in certain areas where it clearly makes sense and in well-defined boundaries. Let’s start with the software development environment that I work with most often, which is Visual Builder Studio. Its design environment uses a modeling tool to model relationships between Business Objects, which is customer data that can have parent-child relationships, and represent and store customer data in tables. It uses a form of entity relationship diagram with cardinality – meaning how many of these are related to how many of those – to model parent-child relationships, including processing requirements like deleting children if a parent is deleted.  The Business Object diagrammer displays your business objects and their relationships, and even lets you create new relationships, modify the business objects, and even create new business objects. You can do all your work in the diagram and the correct code is generated. And you can display the diagram for the code that you created by hand. And the two stay in sync. There’s also a diagramming tool to design the page and page flow navigation between the pages in the web application itself. You can work in code or you can work in the diagram (either one or both), and both are updated at the same time. Visual Builder Studio uses a lot of two-way design and engineering.   15:48 Joe: Visual Builder Studio Page Designer allows you to work in code if you want to write HTML, JavaScript, and JSON code, or work in Design mode and drag and drop components onto the page designer canvas, set properties, and both update each other. It’s very well done. Very well integrated.   Now, oddly enough, even though I am a model-based developer, I find I do most of my work in Visual Builder Studio Designer in the text-based interface because it’s so easy to use. I use the diagrammers to document and share my work, and communicate with other team members and customers. While I think it’s not being used quite so much anymore, Oracle’s JDeveloper and application development framework, ADF, includes built-in tools for doing Unified Modeling Language (UML) modeling. You can create object-oriented class models, generate Java code, reverse engineer Java code, and it updates the model for you. You can also generate the code for mapping Java objects to relational tables. And this has been the heart of data access for ADF Business Components (ADFBC), which is the data layer of Oracle Fusion Apps, for 20 years, although that is being replaced these days. 16:51 Lois: So, these are application development tools for crafting web applications. But do we have any tools like this for the database? Joe: Yes, Lois. We do. Another Oracle tool that uses model-based development functionality is the OCI automated database actions. Here you can define tables, columns, and keys. You can also REST-enable your tables, procedures, and functions.   Oracle SQL Developer for the web is included with OCI or Oracle SQL Developer on the desktop has a robust and comprehensive data modeler that allows you to do full blown entity relationship diagramming and generate code that can be implemented through execution in the database. Now that’s actually the desktop version that has the full-blown diagrammer but you also have some of that in the OCI database actions as well. But the desktop version goes further than that. You can reverse engineer the existing database, generate models from it, modify the models, and then generate the delta, the difference code, to allow you to update an existing database structure based on the change in the model. It is very powerful and highly sophisticated, and I do strongly recommend looking at it.  And Oracle’s APEX (Application Express) has SQL workshop, where you can see a graphic representation of the tables and the relationships between the tables, and even build SQL statements graphically.    18:05 Nikita: It’s time for us to wrap up today but I think it’s safe to say that model-based development tools are still with us. Any final thoughts, Joe?   Joe: Well, actually today I wonder why more people don’t model. I’ve been on multiple projects and worked with multiple clients where there’s no graphical modeling whatsoever—not even a diagram of the database design and the relationships between tables and foreign keys. And I just don’t understand that.   One thing I don’t see very much in current CASE or model-based tools is enabling impact analysis. This is another thing I don’t see a lot. I’ve learned, in many years of working with these tools, to appreciate performing impact analysis. Meaning if I make a change to this thing here, how many other places are going to be impacted? How many other changes am I going to have to make? Something like Visual Builder Studio Designer is very good at this. If you make a change to the spelling of a variable let’s say in one place, it’ll change everywhere that it is referenced and used. And you can do a Find in files to find every place something is used, but it’s still not quite going the full hundred percent and allowing me to do a cross-application impact analysis. If I want to change this one thing here, how many other things will be impacted across applications? But it’s a start. And I will say in talking to the Visual Builder Studio Architect, he understands the value of impact analysis. We’ll see where the tool goes in the future. And this is not a commitment of future direction, of course. It would appear the next step is using AI to listen to our needs and generate the necessary code from it, maybe potentially bypassing models entirely or creating models as a by-product to aid in communication and understanding. We know a picture’s worth a 1000 words and it’s as true today as it’s ever been, and I don’t see that going away anytime soon.   19:41 Lois: Thanks a lot, Joe! It’s been so nice to hear about your journey and learn about the history of CASE tools, where they started and where they are now.  Joe: Thanks Lois and Niki. Nikita: Join us next week for our final episode of this series on building the next generation of Oracle Cloud Apps with Visual Builder Studio. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 20:03 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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9 Apr 202420min