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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AI Innovation at CES - TWiML Talk #222

AI Innovation at CES - TWiML Talk #222

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Self-Tuning Services via Real-Time Machine Learning with Vladimir Bychkovsky - TWiML Talk #221

Today we’re joined by Vladimir Bychkovsky, Engineering Manager at Facebook, to discuss Spiral, a system they’ve developed for self-tuning high-performance infrastructure services at scale, using real-...

17 Tammi 201946min

Building a Recommender System from Scratch at 20th Century Fox with JJ Espinoza - TWiML Talk #220

Building a Recommender System from Scratch at 20th Century Fox with JJ Espinoza - TWiML Talk #220

Today we’re joined by JJ Espinoza, former Director of Data Science at 20th Century Fox. In this talk we dig into JJ and his team’s experience building and deploying a content recommendation system fr...

14 Tammi 201934min

Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219

Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219

Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University. Solon and I caught up to discuss his work on model interpretability and the legal and policy imp...

10 Tammi 201946min

Trends in Computer Vision with Siddha Ganju - TWiML Talk #218

Trends in Computer Vision with Siddha Ganju - TWiML Talk #218

In the final episode of our AI Rewind series, we’re excited to have Siddha Ganju back on the show. Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trend...

7 Tammi 201932min

Trends in Reinforcement Learning with Simon Osindero - TWiML Talk #217

Trends in Reinforcement Learning with Simon Osindero - TWiML Talk #217

In this episode of our AI Rewind series, we introduce a new friend of the show, Simon Osindero, Staff Research Scientist at DeepMind. We discuss trends in Deep Reinforcement Learning in 2018 and beyo...

3 Tammi 201952min

Trends in Natural Language Processing with Sebastian Ruder - TWiML Talk #216

Trends in Natural Language Processing with Sebastian Ruder - TWiML Talk #216

In this episode of our AI Rewind series, we’ve brought back recent guest Sebastian Ruder, PhD Student at the National University of Ireland and Research Scientist at Aylien, to discuss trends in Natur...

31 Joulu 201852min

Trends in Machine Learning with Anima Anandkumar - TWiML Talk #215

Trends in Machine Learning with Anima Anandkumar - TWiML Talk #215

In this episode of our AI Rewind series, we’re back with Anima Anandkumar, Bren Professor at Caltech and now Director of Machine Learning Research at NVIDIA. Anima joins us to discuss her take on tr...

27 Joulu 201851min

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