Introduction to MySQL

Want to gain insights into how virtual nodes provide a serverless Kubernetes experience? Join hosts Lois Houston and Nikita Abraham, along with senior OCI instructor Mahendra Mehra, as they compare managed nodes and virtual nodes. Continuing from the previous episode, they explore how virtual nodes enhance Kubernetes deployments in Oracle Cloud Infrastructure. OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836 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, Radhika Banka, 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! 00:25 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 examined OCI Container Engine for Kubernetes, including its key features and benefits. Lois: Yeah, that was an interesting one. Today, we're going to discuss virtual nodes and their role in enhancing Kubernetes deployments in Oracle Cloud Infrastructure. Nikita: We're going to compare virtual nodes and managed nodes, and look at their differences and advantages. To take us through all this, we have Mahendra Mehra with us. Mahendra is a senior OCI instructor with Oracle University. 01:09 Lois: Hi Mahendra! From our discussion last week, we know that when creating a node pool with Container Engine for Kubernetes, we have the option of specifying the type of Oracle nodes as either managed nodes or virtual nodes. But I'm sure there are some key differences in the features supported by each type, right? Mahendra: The primary point of differentiation between virtual nodes and managed nodes is in their management approach. When it comes to managed nodes, users are responsible for managing the nodes. They have the flexibility to configure them to meet the specific requirements. Users are also responsible for upgrading Kubernetes on managed nodes and for managing cluster capacity. You can create managed nodes and node pools in both basic clusters and enhanced clusters, whereas in virtual nodes, virtual nodes provide a serverless Kubernetes, experience, enabling users to run containerized applications at scale. The Kubernetes software is upgraded and security patches are applied while respecting application's availability requirements. You can only create virtual nodes and virtual node pools in enhanced clusters. 02:17 Nikita: What about differences in terms of resource allocation? Are there any differences we should be aware of? Mahendra: When it comes to managed nodes, the resource allocation is at the node pool level and the users specify CPU and memory resource requirements for a given node pool. In the virtual nodes, the resource allocation is done at the pod level, where you can specify the CPU and memory resource requirements, but this time, as requests and limits in the pod specification. 02:45 Lois: What about differences in the approach to load balancing? Mahendra: When it comes to managed nodes, load balancing is between the worker nodes, whereas in virtual nodes, load balancing is between pods. Also, load balancer security list management is never enabled, and you always must manually configure security rules. When using virtual nodes, load balances distribute traffic among pods' IP addresses and then assign node port. 03:12 Lois: And when it comes to pod networking? Mahendra: Under managed nodes, both the VCN-Native Pod Networking CNI plugin and the flannel CNI plugin are supported. When it comes to virtual nodes, only VCN-Native Pod Networking is supported. Also, only one VNIC is attached to each virtual node. Remember, IP addresses are not pre-allocated before pods are created. And the VCN-Native Pod Networking CNI plugin is not shown as running in the kube-system namespace. Pod subnet route tables must have route rules defined for a NAT gateway and a service gateway. 03:48 Nikita: OK… I have a question, Mahendra. When it comes to scaling Kubernetes clusters and node pools, can users adjust the cluster capacity in response to their changing requirements? Mahendra: When it comes to managed nodes, customers can scale the cluster and node pool up and down by changing the number of managed node pools and nodes respectively. They also have an option to enable autoscaling to automatically scale managed node pools and pods. When it comes to virtual nodes, operational overhead of cluster capacity management is handled for you by OCI. A virtual node pool scales automatically and can support up to 1000 pods per virtual node. Users also have an option to increase the number of virtual node pools or virtual nodes to scale up the cluster or node pool respectively. 04:37 Lois: And what about the pricing for each? Mahendra: Under managed nodes, you pay for the compute instances that execute applications, whereas under virtual nodes, you pay for the exact compute resources consumed by each Kubernetes pod. 04:55 Do you want to stay ahead of the curve in the ever-evolving AI landscape? Look no further than our brand-new OCI Generative AI Professional course and certification. For a limited time only, we're offering both the course and certification for free! So, don't miss out on this exclusive opportunity to get certified on Generative AI at no cost. Act fast because this offer is valid only until July 31, 2024. Visit https://education.oracle.com/genai to get started. That's https://education.oracle.com/genai. 05:34 Nikita: Welcome back! We were just discussing how when you have to choose between virtual nodes and managed nodes for your Kubernetes cluster, you need to consider several key points of differentiation, like the management approach, resource allocation, load balancing, pod networking, scaling, and pricing. Lois: Yeah, it's important to understand the benefits and drawbacks of each approach to make informed decisions. Mahendra, now let's talk about the prerequisites to configure clusters with virtual nodes and the IAM policies that are required to use virtual nodes. Mahendra: Before you can use virtual nodes, you always have to set up at least one IAM policy, which is required in all circumstances by both tenancy administrators and non-administrator users. This basically means, to create and use clusters with virtual nodes and virtual node pools, you must endorse Container Engine for Kubernetes service to allow virtual nodes to create container instances in the Container Engine for Kubernetes service tenancy with a VNIC connected to a subnet of a VCN in your tenancy. All you need to do is create a policy in the root compartment with policy statements from the official documentation page. You will find them under the Working with Virtual Nodes section within the Container Engine topic. 06:55 Lois: Mahendra, how do you create and configure virtual nodes and virtual node pools? Mahendra: Creating virtual nodes is a pivotal step and it involves setting up a virtual node pool in a new cluster. This is exclusively applicable to enhanced clusters. You can initiate this process using the console, the CLI, or the API. Configuring your virtual node pools involves defining critical parameters. Firstly, we have the node count. This represents the number of virtual nodes you wish to create within your virtual node pool. These nodes will be strategically placed in the availability domains that you specify. Now, it's important to carefully consider the placement of these nodes. You can distribute them across different availability domains, ensuring high availability for your applications. Additionally, you have the option to place these nodes in a regional subnet, which is the recommended approach for optimal performance. 07:53 Nikita: Isn't the pod shape another important parameter? Can you tell us a bit about it? Mahendra: Pod shape refers to the type of shape you want for pods running on your virtual nodes within the virtual node pool. The pod shape is crucial as it determines the processor type on which you want your pods to run. It is important to note that only shapes available in your tenancy and supported by Container Engine for Kubernetes will be shown. So choose a shape that aligns with the requirements of your applications and services. A noteworthy point is that you explicitly specify the CPU and memory resource requirements for virtual nodes in the pod specification file. This ensures that your virtual nodes have the necessary resources to handle the workloads of your applications. Precision in specifying these requirements is key to achieving optimal performance. 08:49 Lois: What is the network setup for virtual nodes? Mahendra: The pod running on virtual nodes utilize VCN-native pod networking, and it's crucial to specify how these pods in the node pool communicate with each other. This involves setting up a pod subnet, which is a regional subnet configured specially to host pods. The pod subnet you specify for virtual nodes must be private. Oracle recommends that the pod subnet and the virtual node subnets are the same. In addition to subnet configurations, you have the option to use security rules in network security group to control access to the pod subnet. This involves defining security rules within one or more NSGs that you specify with a maximum limit of five network security groups. Also, it is worth noting that using network security group is recommended over using security list. Now, let's shift our focus to virtual node communication. For this, you will configure a virtual node subnet. This subnet can be either a regional subnet, which is recommended, or an availability domain-specific subnet. And it's designed to host your virtual nodes. 10:02 Nikita: What are some key considerations for virtual node subnets? Mahendra: If you've specified load balancer subnets, ensure that the virtual node subnets are different. As with pod communication, Oracle recommends that the pod subnet and the virtual node subnet are the same, with the added condition that the virtual node subnet must be private. 10:23 Lois: Mahendra, can you take us through the fundamental tasks involved in managing virtual nodes and virtual node pools? Mahendra: Whether you're creating a new enhanced cluster using the Console, or looking to scale up an existing one, the creation process is versatile. Creating virtual nodes involves establishing a virtual node pool. Virtual nodes can only be created within enhanced clusters. Listing virtual nodes task offers visibility into virtual nodes within a virtual node pool. Whether you prefer Console, CLI, or the API, you have the flexibility to choose the method that suits your workflow best. For a comprehensive understanding of your virtual node pools, navigate to the Cluster List page, and click on the name of the cluster. This will unveil the specifics of the virtual node pool you are interested in. Now let's talk about updating virtual node pools. Whether your initiating a new enhanced cluster, or expanding an existing one, the update process ensures your cluster aligns with your evolving requirements. You can easily update the virtual node pool's name for clarity. You can also dynamically change the number of virtual nodes to meet the workload demands, and you can fine tune the Node Placement using options like Availability Domain and Fault Domain settings. Moving on to an essential aspect of node pool management, that is deletion. It's crucial to understand that deleting a node pool is a permanent action. Once deleted, the node pool cannot be recovered. 12:04 Lois: Before we wrap up, Mahendra, can you talk about the critical factors when allocating CPU, memory, and storage resources to pods provisioned by virtual nodes within your OKE cluster? Mahendra: To ensure optimal performance, OKE calculates CPU and memory allocations at the pod level, a distinctive feature when using virtual nodes. This approach stands in contrast to the traditional worker node-level allocation. The allocation process takes into account several factors. First one is the CPU and memory requests and limits. These are specified for each container in the pod spec file, if present. Secondly, number of containers in the pod. The total number of containers impacts the overall resource requirements. And kube-proxy and container runtime requirements. A small but essential consideration taking up 0.25 GB of memory and negligible CPU. Pod CPU and memory requests must meet a minimum of 0.125 OCPUs and 0.5 GB of memory. 13:12 Nikita: Thank you, Mahendra, for this really insightful session. If you're interested in learning more about the topics we discussed today, head over to mylearn.oracle.com and search for the OCI Container Engine for Kubernetes Specialist course. Lois: 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 journey into the world of self-managed nodes and discuss how to manage Kubernetes deployments. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 13:45 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.

2 Juli 202414min

Curious about how OCI Container Engine for Kubernetes (OKE) can transform the way your development team builds, deploys, and manages cloud-native applications? Listen to hosts Lois Houston and Nikita Abraham explore OKE's key features and benefits with senior OCI instructor Mahendra Mehra. Mahendra breaks down complex concepts into digestible bits, making it easy for you to understand the magic behind OKE. OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836 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, Radhika Banka, 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! 00:25 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! If you've been listening to us these last few weeks, you'll know we've been discussing containerization, the Oracle Cloud Infrastructure Registry, and the basics of Kubernetes. Today, we'll dive into the world of OCI Container Engine for Kubernetes, also referred to as OKE. Nikita: We're joined by Mahendra Mehra, a senior OCI instructor with Oracle University, who will take us through the key features and benefits of OKE and also talk about working with managed nodes. Hi Mahendra! Thanks for joining us today. 01:09 Lois: So, Mahendra, what is OKE exactly? Mahendra: Oracle Cloud Infrastructure Container Engine for Kubernetes is a fully managed, scalable, and highly available service that empowers you to effortlessly deploy your containerized applications to the cloud. But that's just the beginning. OKE can transform the way you and your development team build, deploy, and manage cloud native applications. 01:36 Nikita: What would you say are some of its most defining features? Mahendra: One of the defining features of OKE is the flexibility it offers. You can specify whether you want to run your applications on virtual nodes or opt for managed nodes. Regardless of your choice, Container Engine for Kubernetes will efficiently provision them within your existing OCI tenancy on Oracle Cloud Infrastructure. Creating OKE cluster is a breeze, and you have a couple of fantastic tools at your disposal-- the console and the rest API. These make it super easy to get started. OKE relies on Kubernetes, which is an open-source system that simplifies the deployment, scaling, and management of containerized applications across clusters of hosts. Kubernetes is an incredible system that groups containers into logical units known as pods. And these pods make managing and discovering your applications very simple. Not to mention, Container Engine for Kubernetes uses Kubernetes versions that are certified as conformant by the Cloud Native Computing Foundation, also abbreviated as CNCF. And here's the icing on the cake. Container Engine for Kubernetes is ISO-compliant. The other two ISO-IEC standards—27001, 27017, and 27018. That's your guarantee of a secure and reliable platform. 03:08 Lois: That's great. But how do you access all this power? Mahendra: You can define and create your Kubernetes cluster using the intuitive console and the robust rest API. Once your clusters are up and running, you can manage them using the Kubernetes command line, also known as kubectl, the user-friendly Kubernetes dashboard, and the powerful Kubernetes API. 03:32 Nikita: I love the idea of an intuitive console and being able to manage everything from a centralized place. Lois: Yeah, that's fantastic! Mahendra, can you talk us through the magic that happens behind the scenes? What's Oracle's role in all this? Mahendra: All the master nodes or control plane nodes are managed by Oracle. This includes components like etcd, the API server, and the controller manager among others. To ensure reliability, we make sure multiple copies of these master components are distributed across different availability domains. And we don't stop there. We also manage the Kubernetes dashboard and even handle the self-healing mechanism of both the cluster and the worker nodes. All of these are meticulously created and managed within your Oracle tenancy. 04:19 Lois: And what happens at the user's end? What is their responsibility? Mahendra: At your end, you have the power to manage your worker nodes. Using different compute shapes, you can create and control them in your own user tenancy. So, as you can see, it's a perfect blend of Oracle's expertise and your control. 04:38 Nikita: So, in your opinion, why should users consider OKE their go-to solution for all things Kubernetes? Mahendra: Imagine a world where building and maintaining Kubernetes environments, be it master nodes or worker nodes, is no longer complex, costly, or even time-consuming. OKE is here to make your life easier by seamlessly integrating Kubernetes with various container life cycle management products, which includes container registries, CI/CD frameworks, networking solutions, storage options, and top-notch security features. And speaking of security, OKE gives you the tools you need to manage and control team access to production clusters, ensuring granular access to Kubernetes cluster in a straightforward process. It empowers developers to deploy containers quickly, provides devops teams with visibility and control for seamless Kubernetes management, and brings together Kubernetes container orchestration with Oracle's advanced cloud infrastructure. This results in robust control, top tier security, IAM, and consistent performance. 05:50 Nikita: OK…a lot of benefits! Mahendra, I know there have been ongoing enhancements to the OKE service. So, when creating a new cluster with Container Engine for Kubernetes, what is the cluster type we should specify? Mahendra: The first type is the basic clusters. Basic clusters support all the core functionality provided by Kubernetes and Container Engine for Kubernetes. Basic clusters come with a service-level objective, but not a financially backed service level agreement. This means that Oracle guarantees a certain level of availability for the basic cluster, but there is no monetary compensation if that level is not met. On the other hand, we have the enhanced clusters. Enhanced clusters support all available features, including features not supported by basic clusters. 06:38 Lois: OK. So, can you tell us more about the features supported by enhanced clusters? Mahendra: As we move towards a more digitized world, the demand for infrastructure continues to rise. However, with virtual nodes, managing the infrastructure of your cluster becomes much simpler. The burden of manually scaling, upgrading, or troubleshooting worker nodes is removed, giving you more time to focus on your applications rather than the underlying infrastructure. Virtual nodes provide a great solution for managing large clusters with a high number of nodes that require frequent updates or scaling. With this feature, you can easily simplify the management of your cluster and focus on what really matters, that is your applications. Managing cluster add-ons can be a daunting task. But with enhanced clusters, you can now deploy and configure them in a more granular way. This means that you can manage both essential add-ons like CoreDNS and kube-proxy as well as a growing portfolio of optional add-ons like the Kubernetes Dashboard. With enhanced clusters, you have complete control over the add-ons you install or disable, the ability to select specific add-on versions, and the option to opt-in or opt-out of automatic updates by Oracle. You can also manage add-on specific customizations to tailor your cluster to meet the needs of your application. 08:05 Lois: Do users need to worry about deploying add-ons themselves? Mahendra: Oracle manages the lifecycle of add-ons so that you don't have to worry about deploying them yourself. This level of control over add-ons gives you the flexibility to customize your cluster to meet the unique needs of your applications, making managing your cluster a breeze. 08:25 Lois: What about scaling? Mahendra: Scaling your clusters to meet the demands of your workload can be a challenging task. However, with enhanced clusters, you can now provision more worker nodes in a single cluster, allowing you to deploy larger workloads on the same cluster which can lead to better resource utilization and lower operational overhead. Having fewer larger environments to secure, monitor, upgrade, and manage is generally more efficient and can help you save on cost. Remember, there are limits to the number of worker nodes supported on an enhanced cluster, so you should review the Container Engine for Kubernetes limits documentation and consider the additional considerations when defining enhanced clusters with large number of managed nodes. 09:09 Nikita: Ensuring the security of my cluster would be of utmost importance to me, right? How would I do that with enhanced clusters? Mahendra: With enhanced clusters, you can now strengthen cluster security through the use of workload identity. Workload identity enables you to define OCI IAM policies that authorize specific pods to make OCI API calls and access OCI resources. By scoping the policies to Kubernetes service account associated with application pods, you can now allow the applications running inside those pods to directly access the API based on the permissions provided by the policies. 09:48 Nikita: Mahendra, what type of uptime and server availability benefits do enhanced clusters provide? Mahendra: You can now rely on a financially backed service level agreement tied to Kubernetes API server uptime and availability. This means that you can expect a certain level of uptime and availability for your Kubernetes API server, and if it degrades below the stated SLA, you'll receive compensation. This provides an extra level of assurance and helps ensure that your cluster is highly available and performant. 10:20 Lois: Mahendra, do you have any tips for us to remember when creating basic and enhanced clusters? Mahendra: When using the console to create a cluster, a new cluster is created as an enhanced cluster by default unless you explicitly choose to create a basic cluster. If you don't select any enhanced features during cluster creation, you have the option to create the new cluster as a basic cluster. When using CLI or API to create a cluster, you can specify whether to create a basic cluster or an enhanced cluster. If you don't explicitly specify the type of cluster to create, a new cluster is created as a basic cluster by default. Creating a new cluster as an enhanced cluster enables you to easily add enhanced features later even if you didn't select any enhanced features initially. If you do choose to create a new cluster as a basic cluster, you can still choose to upgrade the basic cluster to an enhanced cluster later on. However, you cannot downgrade an enhanced cluster to a basic cluster. These points are really important while you consider selection of a basic cluster or an enhanced cluster for your usage. 11:34 Do you want to stay ahead of the curve in the ever-evolving AI landscape? Look no further than our brand-new OCI Generative AI Professional course and certification. For a limited time only, we're offering both the course and certification for free! So, don't miss out on this exclusive opportunity to get certified on Generative AI at no cost. Act fast because this offer is valid only until July 31, 2024. Visit https://education.oracle.com/genai to get started. That's https://education.oracle.com/genai. 12:13 Nikita: Welcome back! I want to move on to serverless Kubernetes with virtual nodes. But I think before we do that, we first need to have a basic understanding of what managed nodes are. Mahendra: Managed nodes run on compute instances within your tenancy, and are at least partly managed by you. In the context of Kubernetes, a node is a compute host that can be either a virtual machine or a bare metal host. As you are responsible for managing managed nodes, you have the flexibility to configure them to meet your specific requirements. You are responsible for upgrading Kubernetes on managed nodes and for managing cluster capacity. Nodes are responsible for running a collection of pods or containers, and they are comprised of two system components: the kubelet, which is the host brain, and the container runtime such as CRI-O, or containerd. 13:07 Nikita: Ok… so what are virtual nodes, then? Mahendra: Virtual nodes are fully managed and highly available nodes that look and act like real nodes to Kubernetes. They are built using the open source CNCF Virtual Kubelet Project, which provides the translation layer between OCI and Kubernetes. 13:25 Lois: So, what makes Oracle's managed virtual Kubernetes product different? Mahendra: OCI is the first major cloud provider to offer a fully managed virtual Kubelet product that provides a serverless Kubernetes experience through virtual nodes. Virtual nodes are configured by customers and are located within a single availability and fault domain within OCI. Virtual nodes have two main components: port management and container instance management. Virtual nodes delegates all the responsibility of managing the lifecycle of pods to virtual Kubernetes while on a managed node, the kubelet is responsible for managing all the lifecycle state. The key distinction of virtual nodes is that they support up to a 1,000 pods per virtual node with the expectation of supporting more in the future. 14:15 Nikita: What are the other benefits of virtual nodes? Mahendra: Virtual nodes offer a fully managed experience where customers don't have to worry about managing the underlying infrastructure of their containerized applications. Virtual nodes simplifies scaling patterns for customers. Customers can scale their containerized application up or down quickly without worrying about the underlying infrastructure, and they can focus solely on their applications. With virtual nodes, customers only pay for the resources that their containerized application use. This allows customers to optimize their costs and ensures that they are not paying for any unused resources. Virtual nodes can support over 10 times the number of pods that a normal node can. This means that customer can run more containerized applications on virtual nodes, which reduces operational burden and makes it easier to scale applications. Customers can leverage container instances in serverless offering from OCI to take advantage of many OCI functionalities natively. These functionalities include strong isolation and ultimate elasticity with respect to compute capacity. 15:26 Lois: When creating a cluster using Container Engine for Kubernetes, we have the flexibility to customize the worker nodes within the cluster, right? Could you tell us more about this customization? Mahendra: This customization includes specifying two key elements. Firstly, you can select the operating system image to be used for worker nodes. This image serves as a template for the worker node's virtual hard drive, and determines the operating system and other software installed. Secondly, you can choose the shape for your worker nodes. The shape defines the number of CPUs and the amount of memory allocated to each instance, ensuring it meets your specific requirements. This customization empowers you to tailor your OKE cluster to your exact needs. It is important to note that you can define and create OKE clusters using both the console and the REST API. This level of control is specially valuable for your development team when building, deploying, and managing cloud native applications. You have the option to specify whether applications should run on virtual nodes or managed nodes. And Container Engine for Kubernetes efficiently provisions them on Oracle Cloud Infrastructure within your existing OCI tenancy. This flexibility ensures that you can adapt your OKE cluster to suit the specific requirements of your projects and workloads. 16:56 Lois: Thank you so much, Mahendra, for giving us your time today. For more on the topics we discussed, visit mylearn.oracle.com and look for the OCI Container Engine for Kubernetes Specialist course. Join us next week as we dive deeper into working with OKE virtual nodes. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 17:18 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.

25 Juni 202417min

In this episode, Lois Houston and Nikita Abraham, along with senior OCI instructor Mahendra Mehra, dive into the fundamentals of Kubernetes. They talk about how Kubernetes tackles challenges in deploying and managing microservices, and enhances software performance, flexibility, and availability. OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836 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, Radhika Banka, 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 Lois: Hello and welcome to another episode of 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! We've spent the last two episodes getting familiar with containerization and the Oracle Cloud Infrastructure Registry. Today, it's going to be all about Kubernetes. So if you've heard of Kubernetes but you don't know what it is, or you've been playing with Docker and containers and want to know how to take it to the next level, you'll want to stay with us. Lois: That's right, Niki. We'll be chatting with Mahendra Mehra, a senior OCI instructor with Oracle University, about the challenges in containerized applications within a complex business setup and how Kubernetes facilitates container orchestration and improves its effectiveness, resulting in better software performance, flexibility, and availability. 01:20 Nikita: Hi Mahendra. To start, can you tell us when you would use Kubernetes? Mahendra: While deploying and managing microservices in a distributed environment, you may run into issues such as failures or container crashes. Issues such as scheduling containers to specific machines depending upon the configuration. You also might face issues while upgrading or rolling back the applications which you have containerized. Scaling up or scaling down containers across a set of machines can be troublesome. 01:50 Lois: And this is where Kubernetes helps automate the entire process? Mahendra: Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. You can think of a Kubernetes as you would a conductor for an orchestra. Similar to how a conductor would say how many violins are needed, which one play first, and how loud they should play, Kubernetes would say, how many webserver front-end containers or back-end database containers are needed, what they serve, and how many resources are to be dedicated to each one. 02:27 Nikita: That's so cool! So, how does Kubernetes work? Mahendra: In Kubernetes, there is a master node, and there are multiple worker nodes. Each worker node can handle multiple pods. Pods are just a bunch of containers clustered together as a working unit. If a worker node goes down, Kubernetes starts new pods on the functioning worker node. 02:47 Lois: So, the benefits of Kubernetes are… Mahendra: Kubernetes can containerize applications of any scale without any downtime. Kubernetes can self-heal containerized applications, making them resilient to unexpected failures. Kubernetes can autoscale containerized applications as for the workload and ensure optimal utilization of cloud resources. Kubernetes also greatly simplifies the process of deployment operations. With Kubernetes, however complex an operation is, it could be performed reliably by executing a couple of commands at the most. 03:19 Nikita: That's great. Mahendra, can you tell us a bit about the architecture and main components of Kubernetes? Mahendra: The Kubernetes cluster has two main components. One is the control plane, and one is the data plane. The control plane hosts the components used to manage the Kubernetes cluster. And the data plane basically hosts all the worker nodes that can be virtual machines or physical machines. These worker nodes basically host pods which run one or more containers. The containers running within these pods are making use of Docker images, which are managed within the image registry. In case of OCI, it is the container registry. 03:54 Lois: Mahendra, you mentioned nodes and pods. What are nodes? Mahendra: It is the smallest unit of computing hardware within the Kubernetes. Its work is to encapsulate one or more applications as containers. A node is a worker machine that has a container runtime environment within it. 04:10 Lois: And pods? Mahendra: A pod is a basic object of Kubernetes, and it is in charge of encapsulating containers, storage resources, and network IPs. One pod represents one instance of an application within Kubernetes. And these pods are launched in a Kubernetes cluster, which is composed of nodes. This means that a pod runs on a node but can easily be instantiated on another node. 04:32 Nikita: Can you run multiple containers within a pod? Mahendra: A pod can even contain more than one container if these containers are relatively tightly coupled. Pod is usually meant to run one application container inside of it, but you can run multiple containers inside one pod. Usually, it is only the case if you have one main application container and a helper container or some sidecar containers that has to run inside of that pod. Every pod is assigned a unique private IP address, using which the pods can communicate with one another. Pods are meant to be ephemeral, which means they die easily. And if they do, upon re-creation, they are assigned a new private IP address. In fact, Kubernetes can scale a number of these pods to adapt for the incoming traffic, consequently creating or deleting pods on demand. Kubernetes guarantees the availability of pods and replicas specified, but not the liveliness of each individual pod. This means that other pods that need to communicate with this application or component cannot rely on the underlying individual pod's IP address. 05:35 Lois: So, how does Kubernetes manage traffic to this indecisive number of pods with changing IP addresses? Mahendra: This is where another component of Kubernetes called services comes in as a solution. A service gets allocated a virtual IP address and lives until explicitly destroyed. Requests to the services get redirected to the appropriate pods, thus the services of a stable endpoint used for inter-component or application communication. And the best part here is that the lifecycle of service and the pods are not connected. So even if the pod dies, the service and the IP address will stay, so you don't have to change their endpoints anymore. 06:13 Nikita: What types of services can you create with Kubernetes? Mahendra: There are two types of services that you can create. The external service is created to allow external users to connect the containerized applications within the pod. Internal services can also be created that restrict the communication within the cluster. Services can be exposed in different ways by specifying a particular type. 06:33 Nikita: And how do you define these services? Mahendra: There are three types in which you can define services. The first one is the ClusterIP, which is the default service type that exposes services on an internal IP within the cluster. This type makes the service only reachable from within the cluster. You can specify the type of service as NodePort. NodePort basically exposes the service on the same port of each selected node in the cluster using a network address translation and makes the service accessible from the outside of the cluster using the node IP and the NodePort combination. This is basically a superset of ClusterIP. You can also go for a LoadBalancer type, which basically creates an external load balancer in the current cloud. OCI supports LoadBalancer types. It also assigns a fixed external IP to the service. And the LoadBalancer type is a superset of NodePort. 07:25 Lois: There's another component called ingress, right? When do you used that? Mahendra: An ingress is used when we have multiple services on our cluster, and we want the user requests routed to the services based on their pod, and also, if you want to talk to your application with a secure protocol and a domain name. Unlike NodePort or LoadBalancer, ingress is not actually a type of service. Instead, it is an entry point that sits in front of the multiple services within the cluster. It can be defined as a collection of routing rules that govern how external users access services running inside a Kubernetes cluster. Ingress is most useful if you want to expose multiple services under the same IP address, and these services all use the same Layer 7 protocol, typically HTTP. 08:10 Lois: Mahendra, what about deployments in Kubernetes? Mahendra: A deployment is an object in Kubernetes that lets you manage a set of identical pods. Without a deployment, you will need to create, update, and delete a bunch of pods manually. With the deployment, you declare a single object in a YAML file, and the object is responsible for creating the pods, making sure they stay up-to-date and ensuring there are enough of them running. You can also easily autoscale your applications using a Kubernetes deployment. In a nutshell, the Kubernetes deployment object lets you deploy a replica set of your pods, update the pods and the replica sets. It also allows you to roll back to your previous deployment versions. It helps you scale a deployment. It also lets you pause or continue a deployment. 08:59 Do you want to stay ahead of the curve in the ever-evolving AI landscape? Look no further than our brand-new OCI Generative AI Professional course and certification. For a limited time only, we're offering both the course and certification for free! So, don't miss out on this exclusive opportunity to get certified on Generative AI at no cost. Act fast because this offer is valid only until July 31, 2024. Visit https://education.oracle.com/genai to get started. That's https://education.oracle.com/genai. 09:37 Nikita: Welcome back! We were talking about how useful a Kubernetes deployment is in scaling operations. Mahendra, how do pods communicate with each other? Mahendra: Pods communicate with each other using a service. For example, my application has a database endpoint. Let's say it's a MySQL service that it uses to communicate with the database. But where do you configure this database URL or endpoints? Usually, you would do it in the application properties file or as some kind of an external environment variable. But usually, it's inside the build image of the application. So for example, if the endpoint of the service or the service name, in this case, changes to something else, you would have to adjust the URL in the application. And this will cause you to rebuild the entire application with a new version, and you will have to push it to the repository. You'll then have to pull that new image into your pod and restart the whole thing. For a small change like database URL, this is a bit tedious. So for that purpose, Kubernetes has a component called ConfigMap. ConfigMap is a Kubernetes object that maintains a key value store that can easily be used by other Kubernetes objects, such as pods, deployments, and services. Thus, you can define a ConfigMap composed of all the specific variables for your environment. In Kubernetes, now you just need to connect your pod to the ConfigMap, and the pod will read all the new changes that you have specified within the ConfigMap, which means you don't have to go on to build a new image every time a configuration changes. 11:07 Lois: So then, I'm just wondering, if we have a ConfigMap to manage all the environment variables and URLs, should we be passing our username and password in the same file? Mahendra: The answer is no. Password or other credentials within a ConfigMap in a plain text format would be insecure, even though it's an external configuration. So for this purpose, Kubernetes has another component called secret. Kubernetes secrets are secure objects which store sensitive data, such as passwords, OAuth tokens, and SSH keys with the encryption within your cluster. Using secrets gives you more flexibility in a pod lifecycle definition and control over how sensitive data is used. It reduces the risk of exposing the data to unauthorized users. 11:50 Nikita: So, you're saying that the secret is just like ConfigMap or is there a difference? Mahendra: Secret is just like ConfigMap, but the difference is that it is used to store secret data credentials, for example, database username and passwords, and it's stored in the base64 encoded format. The kubelet service stores this secret into a temporary file system. 12:11 Lois: Mahendra, how does data storage work within Kubernetes? Mahendra: So let's say we have this database pod that our application uses, and it has some data or generates some data. What happens when the database container or the pod gets restarted? Ideally, the data would be gone, and that's problematic and inconvenient, obviously, because you want your database data or log data to be persisted reliably for long term. To achieve this, Kubernetes has a solution called volumes. A Kubernetes volume basically is a directory that contains data accessible to containers in a given pod within the Kubernetes platform. Volumes provide a plug-in mechanism to connect ephemeral containers with persistent data stores elsewhere. The data within a volume will outlast the containers running within the pod. Containers can shut down and restart because they are ephemeral units. Data remains saved in the volume even if a container crashes because a container crash is not enough to cut off a pod from a node. 13:10 Nikita: Another main component of Kubernetes is a StatefulSet, right? What can you tell us about it? Mahendra: Stateful applications are applications that store data and keep tracking it. All databases such as MySQL, Oracle, and PostgreSQL are examples of Stateful applications. In a modern web application, we see stateless applications connecting with Stateful application to serve the user request. For example, a Node.js application is a stateless application that receives new data on each request from the user. This application is then connected with a Stateful application, such as MySQL database, to process the data. MySQL stores the data and keeps updating the database on the user's request. Now, assume you deployed a MySQL database in the Kubernetes cluster and scaled this to another replica, and a frontend application wants to access the MySQL cluster to read and write data. The read request will be forwarded to both these pods. However, the write request will only be forwarded to the first primary pod. And the data will be synchronized with other pods. You can achieve this by using the StatefulSets. Deleting or scaling down a StatefulSet will not delete the volumes associated with the Stateful applications. This gives you your data safety. If you delete the MySQL pod or if the MySQL pod restarts, you can have access to the data in the same volume. So overall, a StatefulSet is a good fit for those applications that require unique network identifiers; stable persistent storage; ordered, graceful deployment and scaling; as well as ordered, automatic rolling updates. 14:43 Lois: Before we wrap up, I want to ask you about the features of Kubernetes. I'm sure there are countless, but can you tell us the most important ones? Mahendra: Health checks are used to check the container's readiness and liveness status. Readiness probes are intended to let Kubernetes know if the app is ready to serve the traffic. Networking plays a significant role in container orchestration to isolate independent containers, connect coupled containers, and provide access to containers from the external clients. Service discovery allows containers to discover other containers and establish connections to them. Load balancing is a dedicated service that knows which replicas are running and provides an endpoint that is exposed to the clients. Logging allows us to oversee the application behavior. The rolling update allows you to update a deployed containerized application with minimal downtime using different update scenarios. The typical way to update such an application is to provide new images for its containers. Containers, in a production environment, can grow from few to many in no time. Kubernetes makes managing multiple containers an easy task. And lastly, resource usage monitoring-- resources such as CPU and RAM must be monitored within the Kubernetes environment. Kubernetes resource usage looks at the amount of resources that are utilized by a container or port within the Kubernetes environment. It is very important to keep an eye on the resource usage of the pods and containers as more usage translates to more cost. 16:18 Nikita: I think we can wind up our episode with that. Thank you, Mahendra, for joining us today. Kubernetes sure can be challenging to work with, but we covered a lot of ground in this episode. Lois: That's right, Niki! If you want to learn more about the rich features Kubernetes offers, visit mylearn.oracle.com and search for the OCI Container Engine for Kubernetes Specialist course. Remember, all the training is free, so you can dive right in! Join us next week when we'll take a look at the fundamentals of Oracle Cloud Infrastructure Container Engine for Kubernetes. Until then, Lois Houston… Nikita: And Nikita Abraham, signing off! 16:57 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.

18 Juni 202417min

In this episode, hosts Lois Houston and Nikita Abraham, along with senior OCI instructor Mahendra Mehra, discuss how Oracle Cloud Infrastructure Registry simplifies the development-to-production workflow for developers. Listen to Mahendra explain important container registry concepts, such as images, repositories, image tags, and image paths, as well as how they relate to each other. OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836 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, Radhika Banka, 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 I'm joined by Lois Houston, Director of Innovation Programs. Lois: Hi there! This is our second episode on OCI Container Engine for Kubernetes, and today we're going to spend time discussing container registries with our colleague and senior OCI instructor, Mahendra Mehra. Nikita: We'll talk about how you can become proficient in managing Oracle Cloud Infrastructure Registry, a vital component in your container workflow. 00:58 Lois: Hi Mahendra, can you explain what Oracle Cloud Infrastructure Registry, or OCIR, is and how it simplifies the container image management process? Mahendra: OCIR is an Oracle-managed registry designed to simplify the development-to-production workflow for developers. It offers a range of functionalities, serving as a private docker registry for internal use where developers can easily store, share, and manage container images. The strength of OCIR lies in its high available and scalable architecture. Leveraging OCI to ensure reliable deployment of applications, developers can use OCIR not only as a private registry but also as a public registry, facilitating the pulling of images from public repositories for users with internet access. 01:55 Lois: But what sets OCIR apart? Mahendra: What sets OCIR apart is its compliance with the Open Container Initiative standards, allowing the storage of container images conforming to the OCI specifications. It goes a step further by supporting manifest lists, sometimes known as multi-architecture images, accommodating diverse architectures like ARM and AMD64. Additionally, OCIR extends its support to Helm charts. Security is a priority with OCIR, offering private access through a service gateway. This means that OCI resources within a VCN in the same region can securely access OCIR without exposing them to the public internet. 02:46 Nikita: OK. What are some other key advantages of OCIR? Mahendra: Firstly, OCIR seamlessly integrates with the Container Engine for Kubernetes, ensuring a cohesive container management experience. In terms of security, OCIR provides flexibility by allowing registries to be either private or public, giving administrators control over accessibility. It is intricately integrated with IAM, offering straightforward authentication through OCI Identity. Another notable benefit is regional availability. You can efficiently pull container images from the same region as your deployments. For high-performance, availability, and low-latency image operations, OCIR leverages the robust infrastructure of OCI, enhancing the overall reliability of image push and pull operations. OCIR ensures anywhere access, allowing you to utilize container CLI for image operations from various locations, be it on the cloud, on-premises, or even from personal laptops. 03:57 Lois: I believe OCIR has repository quotas? Is there a cap on them? Mahendra: In each enabled region for your tenancy, you can establish up to 500 repositories with a cumulative storage limit of 500 GB. Each repository is capable of holding up to 100,000 images. Importantly, charges apply only for stored images. 04:21 Nikita: That's good to know, Mahendra. I want to move on to basic container registry concepts. Maybe we can start with what an image is. Mahendra: Image is basically a read-only template with instructions for creating a container. It holds the application that you want to run as a container, along with any dependencies that are required. Container registry is an Open Container Initiative-compliant registry. As a result, you can store any artifacts that conform to Open Container Initiative specifications, such as Docker images, manifest lists, sometimes also known as multi-architecture images, and Helm charts. 05:02 Lois: And what's a repository then? Mahendra: It's a meaningfully named collection of related images which are grouped together for convenience in a container registry. There are different versions of the same source image, which are grouped together into the same repository. You can have multiple images stored under this repository. The only thing that you need to keep changing is the image version. Every image version is given a tag. And the tag uniquely identifies the image. 05:33 Lois: Is it possible to make the repository public or private? Mahendra: Depending upon your need, a repository can be made private or public. One important thing to note is that the user needs to have an OCI username and authentication token before being able to push/pull an image from the OCIR. 05:52 Nikita: There are so many terms that you come across when working with repositories and container registry, right? Could you take us through them and explain how they relate to each other? I've heard of the region key and tenancy namespace. Mahendra: The region key identifies the container registry region that you are using. A tenancy namespace is an auto-generated random and immutable string of alphanumeric characters. The tenancy namespace can be retrieved from the value of your object storage namespace field. Repository name is the name of a repository in container registry, to and from which you can push and pull images. Repository names can include one or more slash characters and are unique across all the compartments in the entire tenancy. You should note that although a repository name can include slash characters, the slash does not represent a hierarchical directory structure. It is simply one character in the string of characters. As a convenience, you might choose to start the name of different repositories with the same string. A registry identifier is the combination of your container registry region key and the tenancy namespace. 07:07 Lois: What about an image tag and an image path? How do they differ from each other? Mahendra: A tag or an image tag is a string used to refer to a particular image in a known registry. The term "image name" is sometimes used as a shorthand way to refer to a particular image in a particular repository. A tag can be a numerical value or it can be a string. An image path is a fully qualified path to a particular image in a registry. It extends the repository path by adding tags associated with the image. 07:46 Do you want to stay ahead of the curve in the ever-evolving AI landscape? Look no further than our brand-new OCI Generative AI Professional course and certification. For a limited time only, we're offering both the course and certification for free. So, don't miss out on this exclusive opportunity to get certified on Generative AI at no cost. Act fast because this offer is valid only until July 31, 2024. Visit https://education.oracle.com/genai to get started. That's https://education.oracle.com/genai. 08:24 Nikita: Welcome back! Mahendra, from what you've told us, OCIR seems like such a pivotal tool for modern containerized workflows, with its seamless integration, robust security measures, regional accessibility, efficient image management. So, how do we actually manage OCIR? Mahendra: Managing OCIR can be done in three ways. Starting with managing the repository itself, followed by managing the images within the repository, and, last but not the least, managing the overall security of your repository alongside the images. 08:58 Nikita: Can we dive into each of these approaches in a little more detail? How does managing the repository itself work? Mahendra: You can create an empty repository in a compartment and give it a name that's unique across all the compartments in the entire tenancy. There is a limit to the number of repositories you can have in a given region in a tenancy. So, when you no longer need a repository, it makes sense to delete it from the Oracle Cloud Infrastructure registry. Make a note that when you delete a repository, it can take up to 48 hours for the deletion to take effect and for the storage to actually be released. When you create a new repository in Oracle Cloud Infrastructure Registry, you specify the compartment in which you want to create it. Having created the repository in one compartment, you can subsequently move it to a different compartment. The reasons can be many. It can be to change the users who are authorized to use the repository or to change how the billing for a repository is charged. 09:52 Lois: OK. And what about managing images within the repository? Mahendra: You can view the images stored on OCIR using the OCI Console or using Docker images command from your Docker client after logging in to the OCIR repo. To push an image, you first used the Docker tag command to create a copy of the local source image as a new image. As a name for the new image, you specify the fully-qualified path to the target location in your container registry where you want to push the image, including the name of a repository. In order to pull an image, you must be logged in into the OCIR registry using the auth token and use the Docker pull command followed by a fully-qualified name of the image you wish to download on your Docker client. 10:36 Nikita: What happens when you no longer need an old image or you simply want to clean up the list of image tags in a repository? Mahendra: You can delete images from the Oracle Cloud Infrastructure Registry. You can undelete an image you've previously deleted for up to 48 hours after you deleted it. After that time, the image is permanently removed from the container registry. You can set up image retention policies to automatically delete images that meet particular selection criteria. 11:02 Lois: What sort of selection criteria? Mahendra: Criterias can be images that have not been pulled for a certain number of days or images that have not been tagged for a certain number of days. It can also be images that have not been given particular Docker tags specified as exempt from the automatic deletion. There's an hourly process that checks images against the selection criteria, and any that meet the selection criteria are automatically deleted. In each region in a tenancy, there's a global image retention policy. The default criteria of the policy is to retain all images so that no images are automatically deleted. However, you can change the global image retention policy so that the images are deleted if they meet certain criteria that you specify. A region's global image retention policy applies to all the repository within that region unless it is explicitly overridden by one or more custom image retention policies. Only one custom image retention policy at a time can be applied to a repository. If a repository has already been added to a custom retention policy and you want to add repository to a different custom retention policy, you have to remove the policy from the first retention policy before adding it to the second one. 12:15 Lois: Mahendra, what should we keep in mind when we're dealing with the global image retention policy? Mahendra: The global image retention policy are specific to a particular region. To delete images consistently in different regions in your tenancy, you need to set up image retention policies in each region with identical selection criteria. If you want to prevent images from being deleted on the basis of Docker tags they've been given, you need to specify those tags as exempt in a comma-separated list. When you want to clean up the list of images in a repository without actually deleting the images, you can remove the tags from the images in OCIR. Removing images is referred to as untagging. 12:53 Nikita: OK…and the last approach was managing the overall security of your repository alongside the images, right? Mahendra: While managing security, you are given fine grained control over the operations that users are allowed to perform on repositories within the Container Registry. Using the concept of users and groups, you can control repository access by setting up identity access management policies at the tenancy and at the compartment level. You can write policies to allow inspect, read, use, and manage operations on the repository based on the requirements. You can set up Oracle Cloud Infrastructure Registry to scan images in a repository for security vulnerabilities published in the publicly available common vulnerabilities and exposure databases. To perform image scanning, container registry makes use of the Oracle Cloud Infrastructure vulnerability-scanning service and vulnerability scanning REST API. 13:46 Nikita: What do I need to have in place before I can push and pull Docker images to and from Oracle Cloud Infrastructure Registry? Mahendra: The first thing is, your tenancy must be subscribed to one or more of the regions in which the container registry is available. You can check the same within the Oracle documentation. The next thing is, you need to have access to the Docker command line interface to push and pull images on your local machine. The third thing is, users must belong to a group to which a policy grants the appropriate permission or belong to a tenancies administrator group, which by default have access permissions on the container registry. Lastly, user must already have an Oracle Cloud Infrastructure username and an authentication token, which enables them to perform operations on the container registry. 14:29 Lois: Thank you, Mahendra, for sharing your insights on OCIR with us. To watch demos on managing OCIR, visit mylearn.oracle.com and search for the OCI Container Engine for Kubernetes Specialist course. Nikita: Mahendra will be back next week to walk us through the basics of Kubernetes. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 14:53 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.

11 Juni 202415min

Welcome to a new season of the Oracle University Podcast, where we delve deep into the world of OCI Container Engine for Kubernetes. Join hosts Lois Houston and Nikita Abraham as they ask senior OCI instructor Mahendra Mehra about the transformative power of containers in application deployment and why they're so crucial in today's software ecosystem. Uncover key differences between virtualization and containerization, and gain insights into Docker components and commands. Getting Started with Oracle Cloud Infrastructure: https://oracleuniversitypodcast.libsyn.com/getting-started-with-oracle-cloud-infrastructure-1 Networking in OCI: https://oracleuniversitypodcast.libsyn.com/networking-in-oci OCI Identity and Access Management: https://oracleuniversitypodcast.libsyn.com/oci-identity-and-access-management OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836 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, Radhika Banka, 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 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! Welcome to a new season of the Oracle University Podcast. This time around, we're going to delve into the world of OCI Container Engine for Kubernetes, or OKE. For the next couple of weeks, we'll cover key aspects of OKE to help you create, manage, and optimize Kubernetes clusters in Oracle Cloud Infrastructure. 00:58 Lois: So, whether you're a cloud native developer, Kubernetes administrator and developer, a DevOps engineer, or site reliability engineer who wants to enhance your expertise in leveraging the OCI OKE service for cloud native application solutions, you'll want to tune in to these episodes for sure. And if that doesn't sound like you, I'll bet you will find the season interesting even if you're just looking for a deep dive into this service. Nikita: That's right, Lois. In today's episode, we'll focus on concepts of containerization, laying the foundation for your journey into the world of containers. And taking us through all this is Mahendra Mehra, a senior OCI instructor with Oracle University. 01:38 Lois: Hi Mahendra! We're so glad to start our look at containerization with you today. Could you give us an overview? Why is it important in today's software world? Mahendra: Containerization is a form of virtualization, operates by running applications in isolated user spaces known as containers. All these containers share the same underlying operating system. The container engine, pivotal in containerization technologies and container orchestration platforms, serves as the container runtime environment. It effectively manages the creation, deployment, and execution of containers. 02:18 Lois: Can you simplify this for a novice like me, maybe by giving us an analogy? Mahendra: Imagine a container as a fully packaged and portable computing environment. It's like a digital suitcase that holds everything an application needs to run—binaries, libraries, configuration files, dependencies, you name it. And the best part, it's all encapsulated and isolated within container. 02:46 Nikita: Mahendra, how is containerization making our lives easier today? Mahendra: In olden days, running an application meant matching it with your machine's operating system. For example, Windows software required a Windows machine. However, containerization has rewritten this narrative. Now, it's ancient history. With containerization, you create a single software package, a container that gracefully runs on any device or operating systems. What's fascinating is that these containers seamlessly run while sharing the host operating system. The container engine is like a shadow abstracted from the host operating system with limited access to underlying resources. Think of it as a super lightweight virtual machine. The beauty of this, the containerized application becomes a globetrotter, seamlessly running on bare metal within VMs or on the cloud platforms without needing tweaks for each environment. 03:52 Nikita: How is containerization different from traditional virtualization? Mahendra: On one side, we have traditional virtualization. It's like having multiple houses on a single piece of land, and each house or virtual machine has its complete setup—wall, roofs, and utilities. This setup, while providing isolation, can be resource-intensive with each virtual machine carrying its entire operating system. Now, let's shift gears to containerization, the modern day superhero. Imagine a high-rise building where each floor represents a container. These containers share the same building or host operating system, but have their private space or isolated user space. Here's the magic. They are super lightweight, don't carry extra baggage of a full operating system and can swiftly move between different floors. 04:50 Lois: Ok, gotcha. That sounds pretty efficient! So, what are the direct benefits of containerization? Mahendra: With containerization technology, there's less overhead during startup and no need to set up a separate guest OS for each application since they all share the same OS kernel. Because of this high efficiency, containerization is commonly used for packing up the many individual microservices that make up modern applications. Containerization unfolds a spectrum of benefits, delivering unparalleled portability as containers run uniformly across diverse platforms. This agility, fostered by open source container engines, empowers developers with cross-platform flexibility. The speed of containerized applications known for their lightweight nature reduces cost, boosts efficiency, and accelerates start times. Fault isolation ensures robustness, allowing independent operations without affecting others. Efficiency thrives as containers share the OS kernel and reusable layers, optimizing server utilization. The ease of management is achieved through orchestration platforms like Kubernetes automating essential tasks. Security remains paramount as container isolation and defined permissions fortify the infrastructure against malicious threats. Containerization emerges not just as a technology but as a transformative force, redefining how we build, deploy, and manage applications in the digital landscape. 06:37 Lois: It sure makes deployment efficient, scalability, and seamless! Mahendra, various components of Docker architecture work together to achieve containerization goals, right? Can you walk us through them? Mahendra: A developer or a DevOps professional communicates with Docker engine through the Docker client, which may be run on the same computer as Docker engine in case of development environments or through a remote shell. So whenever a developer fires a Docker command, the client sends them to the Docker Daemon which carries them out. The communication between the Docker client and the Docker host is usually taken place through REST APIs. The Docker clients can communicate with more than one Daemon at a time. Docker Daemon is a persistent background process that manages Docker images, containers, networks, and storage volumes. The Docker Daemon constantly listens to the Docker API request from the Docker clients and processes them. Docker registries are services that provide locations from where you can store and download Docker images. In other words, a Docker registry contains repositories that host one or more Docker images. Public registries include Docker Hub and Docker Cloud and private registries can also be used. Oracle Cloud Infrastructure offers you services like OCIR, which is also called a container registry, where you can host your own private or public registry. 08:02 Do you want to stay ahead of the curve in the ever-evolving AI landscape? Look no further than our brand-new OCI Generative AI Professional course and certification. For a limited time only, we're offering both the course and certification for free. So, don't miss out on this exclusive opportunity to get certified on Generative AI at no cost. Act fast because this offer is valid only until July 31, 2024. Visit https://education.oracle.com/genai to get started. That's https://education.oracle.com/genai. 08:39 Nikita: Welcome back! Mahendra, I'm wondering how virtual machines are different from containers. How do virtual machines work? Mahendra: A hypervisor or a virtual machine monitor is a software, firmware, or hardware that creates and runs virtual machines. It is placed between the hardware and the virtual machines, and is necessary to virtualize the server. Within each virtual machine runs a unique guest operating system. VMs with different operating systems can run on the same physical server. A Linux VM can sit alongside a Windows VM and so on. Each VM has its own binaries, libraries, and application that it services. And the VM may be many gigabytes in size. 09:22 Lois: What kind of benefits do we see from virtual machines? Mahendra: This technique provides a variety of benefits like the ability to consolidate applications into a single system, cost savings through reduced footprints, and faster server provisioning. But this approach has its own drawbacks. Each VM includes a separate operating system image, which adds overhead in memory and storage footprint. As it turns out, this issue adds complexity to all the stages of software development lifecycle, from development and test to production and disaster recovery as well. It also severely limits the portability of applications between different cloud providers and traditional data centers. And this is where containers come to the rescue. 10:05 Lois: OK…how do containers help in this situation? Mahendra: Containers sit on top of a physical server and its host operating system—typically, Linux or Windows. Each container shares the host OS kernel and usually the binaries and libraries as well. But the shared components are read only. Sharing OS resources such as libraries significantly reduces the need to reproduce the operating system code. A server can run multiple workloads with a single operating system installation. Containers are thus exceptionally lightweight. They are only megabytes in size and take just seconds to start. What this means in practice is you can put two or three times as many applications on a single server with containers than you can put on a virtual machine. Compared to containers, virtual machines take minutes to run and are order of magnitude larger than an equivalent container measured in gigabytes versus megabytes. 11:01 Nikita: So then, is there ever a time you should use a virtual machine? Mahendra: You should use a virtual machine when you want to run applications that specifically require a new OS, also when isolation and security are your priority over everything else. In most scenarios, a container will provide a lighter, faster, and more cost-effective solution than the virtual machines. 11:22 Lois: Now that we've discussed containerization and the different Docker components, can you tell us more about working with Docker images? We first need to know what a Dockerfile is, right? Mahendra: A Dockerfile is a text file that defines a Docker image. You'll use a Dockerfile to create your own custom Docker image. In other words, you use it to define your custom environment to be used in a Docker container. You'll want to create your own Dockerfile when existing images won't meet your project needs to different runtime requirements, which means that learning about Docker files is an essential part of working with Docker. Dockerfile is a step-by-step definition of building up a Docker image. It provides a set of standard instructions to be used in Dockerfile that Docker will execute when you issue a Docker build command. 12:09 Nikita: Before we wrap up, can you walk us through some Docker commands? Mahendra: Every Dockerfile must start with a FROM instruction. The idea behind this is that you need a starting point to build your image. It can be from scratch or from an existing image available in the Docker registry. The RUN command is used to execute a command and will wait till the command finishes its execution. Since most of the images are Linux-based, a good practice is to set up a directory you will work in. That's the purpose of work directory line. It defines a directory and moves you in. The COPY instruction helps you to copy your source code into the image. ENV provides default values for variables that can be accessed within the containers. If your app needs to be reached from outside the container, you must open its listening port using the EXPOSE command. Once your application is ready to run, the last thing to do is to specify how to execute it. You must add the CMD line with the same command with all the arguments you used locally to launch your application. This command can also be used to execute commands at runtime for the containers, but we can be more flexible using the ENTRYPOINT command. Labels are used in Dockerfile to help organize your Docker images. 13:20 Lois: Thank you, Mahendra, for joining us today. I learned a lot! And if you want to learn more about working with Docker images, go to mylearn.oracle.com and search for the OCI Container Engine for Kubernetes Specialist course. The course is free so you can get started right away. Nikita: Yeah, a fundamental understanding of core OCI services, like Identity and Access Management, networking, compute, storage, and security, is a prerequisite to the course and will certainly serve you well when leveraging the OCI OKE service. And the quickest way to gain this knowledge is by completing the OCI Foundations Associate learning path on MyLearn and getting certified. You can also listen to episodes from our first season, called OCI Made Easy, where we discussed these topics. We'll put a few links in the show notes so you can easily find them. Lois: We're looking forward to having Mahendra join us again next week when we'll talk about container registries. Until next time, this is Lois Houston… Nikita: And Nikita Abraham signing off! 14:24 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.

4 Juni 202414min

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 Maj 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 Maj 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 Maj 202417min