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

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Innhold levert av TWIML and Sam Charrington. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av TWIML and Sam Charrington eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

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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726 episoder

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Manage episode 386382494 series 2355587
Innhold levert av TWIML and Sam Charrington. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av TWIML and Sam Charrington eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

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.

  continue reading

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