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.

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Graph ML Research at Twitter with Michael Bronstein - #394

Graph ML Research at Twitter with Michael Bronstein - #394

Today we’re excited to be joined by return guest Michael Bronstein, Head of Graph Machine Learning at Twitter. In our conversation, we discuss the evolution of the graph machine learning space, his ne...

23 Juli 202055min

Panel: The Great ML Language (Un)Debate! - #393

Panel: The Great ML Language (Un)Debate! - #393

Today we’re excited to bring ‘The Great ML Language (Un)Debate’ to the podcast! In the latest edition of our series of live discussions, we brought together experts and enthusiasts to discuss both pop...

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What the Data Tells Us About COVID-19 with Eric Topol - #392

What the Data Tells Us About COVID-19 with Eric Topol - #392

Today we’re joined by Eric Topol, Director & Founder of the Scripps Research Translational Institute, and author of the book Deep Medicine. We caught up with Eric to talk through what we’ve learned ab...

16 Juli 202042min

The Case for Hardware-ML Model Co-design	with Diana Marculescu - #391

The Case for Hardware-ML Model Co-design with Diana Marculescu - #391

Today we’re joined by Diana Marculescu, Professor of Electrical and Computer Engineering at UT Austin. We caught up with Diana to discuss her work on hardware-aware machine learning. In particular, ...

13 Juli 202045min

Computer Vision for Remote AR with Flora Tasse - #390

Computer Vision for Remote AR with Flora Tasse - #390

Today we conclude our CVPR coverage joined by Flora Tasse, Head of Computer Vision & AI Research at Streem. Flora, a keynote speaker at the AR/VR workshop, walks us through some of the interesting use...

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Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389

Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389

Today we're Julian Quiroga, a Computer Vision Team Lead at Genius Sports, to discuss his recent paper “As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game Sta...

6 Juli 202041min

How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388

How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388

Today we’re taking a break from our CVPR coverage to bring you this interview with Deb Raji, a Technology Fellow at the AI Now Institute. Recently there have been quite a few major news stories in th...

2 Juli 20201h 20min

AI for High-Stakes Decision Making with Hima Lakkaraju - #387

AI for High-Stakes Decision Making with Hima Lakkaraju - #387

Today we’re joined by Hima Lakkaraju, an Assistant Professor at Harvard University. At CVPR, Hima was a keynote speaker at the Fair, Data-Efficient and Trusted Computer Vision Workshop, where she spok...

29 Juni 202045min

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