Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657

Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657

Today we’re joined by Jay Emery, director of technical sales & architecture at Microsoft Azure. In our conversation with Jay, we discuss the challenges faced by organizations when building LLM-based applications, and we explore some of the techniques they are using to overcome them. We dive into the concerns around security, data privacy, cost management, and performance as well as the ability and effectiveness of prompting to achieve the desired results versus fine-tuning, and when each approach should be applied. We cover methods such as prompt tuning and prompt chaining, prompt variance, fine-tuning, and RAG to enhance LLM output along with ways to speed up inference performance such as choosing the right model, parallelization, and provisioned throughput units (PTUs). In addition to that, Jay also shared several intriguing use cases describing how businesses use tools like Azure Machine Learning prompt flow and Azure ML AI Studio to tailor LLMs to their unique needs and processes. The complete show notes for this episode can be found at twimlai.com/go/657.

Jaksot(780)

OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674

OLMo: Everything You Need to Train an Open Source LLM with Akshita Bhagia - #674

Today we’re joined by Akshita Bhagia, a senior research engineer at the Allen Institute for AI. Akshita joins us to discuss OLMo, a new open source language model with 7 billion and 1 billion variants...

4 Maalis 202432min

Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673

Training Data Locality and Chain-of-Thought Reasoning in LLMs with Ben Prystawski - #673

Today we’re joined by Ben Prystawski, a PhD student in the Department of Psychology at Stanford University working at the intersection of cognitive science and machine learning. Our conversation cente...

26 Helmi 202425min

Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672

Reasoning Over Complex Documents with DocLLM with Armineh Nourbakhsh - #672

Today we're joined by Armineh Nourbakhsh of JP Morgan AI Research to discuss the development and capabilities of DocLLM, a layout-aware large language model for multimodal document understanding. Armi...

19 Helmi 202445min

Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671

Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671

Today we’re joined by Sanmi Koyejo, assistant professor at Stanford University, to continue our NeurIPS 2024 series. In our conversation, Sanmi discusses his two recent award-winning papers. First, we...

12 Helmi 20241h 5min

AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670

AI Trends 2024: Reinforcement Learning in the Age of LLMs with Kamyar Azizzadenesheli - #670

Today we’re joined by Kamyar Azizzadenesheli, a staff researcher at Nvidia, to continue our AI Trends 2024 series. In our conversation, Kamyar updates us on the latest developments in reinforcement le...

5 Helmi 20241h 10min

Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669

Building and Deploying Real-World RAG Applications with Ram Sriharsha - #669

Today we’re joined by Ram Sriharsha, VP of engineering at Pinecone. In our conversation, we dive into the topic of vector databases and retrieval augmented generation (RAG). We explore the trade-offs ...

29 Tammi 202435min

Nightshade: Data Poisoning to Fight Generative AI with Ben Zhao - #668

Nightshade: Data Poisoning to Fight Generative AI with Ben Zhao - #668

Today we’re joined by Ben Zhao, a Neubauer professor of computer science at the University of Chicago. In our conversation, we explore his research at the intersection of security and generative AI. W...

22 Tammi 202439min

Learning Transformer Programs with Dan Friedman - #667

Learning Transformer Programs with Dan Friedman - #667

Today, we continue our NeurIPS series with Dan Friedman, a PhD student in the Princeton NLP group. In our conversation, we explore his research on mechanistic interpretability for transformer models, ...

15 Tammi 202438min

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