Generative AI on Microsoft Azure : from large language models to advanced multi-agent systems / by Adrián González Sánchez, Jaime De Mora, and Jorge Garcia Ximenez.

Format:

Book

Author/Creator:

Sánchez, Adrián González, author.

Mora, Jaime De, author.

Ximenez, Jorge Garcia, author.

Language:

English

Subjects (All):

Microsoft Azure (Computing platform).

Cloud computing.

Generative artificial intelligence.

Physical Description:

1 online resource (250 pages)

Edition:

[First edition].

Place of Publication:

Sebastopol, California] : O'Reilly Media, Inc., [2026]

Summary:

Companies are now moving generative AI projects from the lab to production environments. To support these increasingly sophisticated applications, they're turning to advanced practices such as multiagent architectures and complex code-based frameworks. This practical handbook shows you how to leverage cutting-edge techniques using Microsoft's powerful ecosystem of tools to deploy trustworthy AI systems tailored to your organization's needs. Written for and by AI professionals, Generative AI on Microsoft Azure goes beyond the technical core aspects, examining underlying principles, tools, and practices in depth, from the art of prompt engineering to strategies for fine-tuning models to advanced techniques like retrieval-augmented generation (RAG) and agentic AI. Through real-world case studies and insights from top experts, you'll learn how to harness AI's full potential on Azure, paving the way for groundbreaking solutions and sustainable success in today's AI-driven landscape. Understand the technical foundations of generative AI and how the technology has evolved over the last few years Implement advanced GenAI applications using Microsoft services like Azure AI Foundry, Copilot, GitHub Models, Azure Databricks, and Snowflake on Azure Leverage patterns, tools, frameworks, and platforms to customize AI projects Manage, govern, and secure your AI-enabled systems with responsible AI practices Build upon expert guidance to avoid common pitfalls, future-proof your applications, and more.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1572661245