Azure OpenAI for Cloud Native Applications

Azure OpenAI for Cloud Native Applications

by Adrian Gonzalez Sanchez
Released July 2024
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781098154998

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Book description

Get the details, examples, and best practices you need to build cloud native applications, services, and solutions using the power of the Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrian Gonzalez Sanchez examines the integration and utilization of Azure OpenAI—using powerful generative AI models such as GPT-3.5 Turbo and GPT4—within the Microsoft Azure cloud computing platform.

To guide you through the technical details of using Azure OpenAI, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI's language model can be applied, such as natural language processing, chatbots, content generation, translation, sentiment analysis, and more.

Ideal for software and cloud developers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you:

  • Learn how to implement cloud native applications with the Azure OpenAI Service
  • Deploy, customize, and integrate Azure OpenAI with your applications
  • Customize large language models and orchestrate knowledge with company-owned data
  • Use advanced road maps to plan your generative AI project
  • Estimate cost and plan generative AI implementations for adopter companies

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Table of contents

  1. Brief Table of Contents (Not Yet Final)
  2. Preface
  3. Introduction
  4. 1. Introduction to Generative AI and the Azure OpenAI Service
    1. What’s Artificial Intelligence
      1. Current Level of AI Adoption
      2. The Many Technologies of AI
      3. Typical AI Use Cases
      4. Types of AI Learning Approaches
    2. About Generative AI
      1. Main Capabilities
      2. Relevant Industry Actors
      3. The Key Role of Foundation Models
      4. Road to Artificial General Intelligence (?)
    3. Microsoft, OpenAI, and the Azure OpenAI Service
      1. The Rise of the AI Copilots
      2. Azure OpenAI Capabilities and Use Cases
      3. LLM Tokens as the New Unit of Measure
    4. Conclusion
  5. 2. Designing Cloud-Native Architectures for Generative AI
    1. Modernizing Applications to Make Them Generative AI-Ready
    2. Cloud Native Development
      1. Microservice-based Apps and Containers
      2. Serverless Workflows
      3. Azure-based Web Development and CI/CD
    3. Understanding the Azure Portal
    4. General Azure OpenAI Considerations
      1. Available Azure OpenAI Models
      2. Architectural Elements for Generative AI Systems
    5. Conclusion
  6. 3. Implementing Cloud-Native Generative AI with Azure OpenAI
    1. Defining the Knowledge Scope of Azure OpenAI-enabled Apps
    2. Generative AI Modelling with Azure OpenAI
      1. Azure OpenAI Service Building Blocks
      2. Potential Implementation Approaches
      3. Approach Comparison and Final Recommendation
      4. AI Performance Evaluation Methods
    3. Conclusion
  7. 4. Additional Cloud and AI capabilities
    1. Plugins
    2. LLM Development, Orchestration, and Integration
      1. LangChain
      2. Semantic Kernel
      3. Bot Framework
      4. Power Platform, Power Virtual Agents, and AI Builder
    3. Databases / Vector Stores
      1. Vector Search from Azure Cognitive Search
      2. Vector Search from CosmosDB
      3. Redis Databases on Azure
      4. Other Relevant Databases (Including Open Source)
    4. Others Microsoft Building Blocks for Generative AI
      1. Azure AI Document Intelligence (Formerly Azure Form Recognizer) for OCR
      2. Ongoing Microsoft Open Source and Research Projects
    5. Conclusion
  8. 5. Operationalizing Generative AI Implementations
    1. The Art of Prompt Engineering
    2. Generative AI and LLMOps
      1. Prompt Flow and Azure ML
      2. Securing LLMs
      3. Managing Privacy and Compliance
    3. Responsible AI and new regulations
      1. Relevant Regulatory Context for Generative AI Systems
      2. Company-level AI Governance Resources
      3. Technical-level Responsible AI tools
    4. Conclusion
  9. 6. Elaborating Generative AI Business Cases
    1. Pre-Mortem or What to Consider Before Implementing a Generative AI Project
    2. Defining Implementation Approach, Resources, and Project Roadmap
      1. Defining Project Workstreams
      2. Identifying Required Resources
      3. Estimating Duration and Effort
      4. Creating a “Living” Roadmap
    3. Creating Usage Scenarios
    4. Calculating Cost and Potential ROI
    5. Conclusion
  10. About the Author

Product information

  • Title: Azure OpenAI for Cloud Native Applications
  • Author(s): Adrian Gonzalez Sanchez
  • Release date: July 2024
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098154998