
Azure OpenAI Service for Cloud Native Applications
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Content
- Intro
- Copyright
- Table of Contents
- Preface
- How This Book Is Organized
- Conventions Used in This Book
- Using Code Examples
- O'Reilly Online Learning
- How to Contact Us
- Acknowledgments
- Introduction
- Chapter 1. Introduction to Generative AI and Azure OpenAI Service
- What Is Artificial Intelligence?
- Current Level of AI Adoption
- The Many Technologies of AI
- Typical AI Use Cases
- Types of AI Learning Approaches
- About Generative AI
- Primary Capabilities of Generative AI
- Relevant Industry Actors
- The Key Role of Foundation Models
- Road to Artificial General Intelligence
- Microsoft, OpenAI, and Azure OpenAI Service
- The Rise of AI Copilots
- Azure OpenAI Service Capabilities and Use Cases
- LLM Tokens as the New Unit of Measure
- Conclusion
- Chapter 2. Designing Cloud Native Architectures for Generative AI
- Modernizing Applications for Generative AI
- Cloud Native Development with Azure OpenAI Service
- Microservice-Based Apps and Containers
- Serverless Workflows
- Azure-Based Web Development and CI/CD
- Understanding the Azure Portal
- General Azure OpenAI Service Considerations
- Available Azure OpenAI Service Models
- Architectural Elements of Generative AI Systems
- Conclusion
- Chapter 3. Implementing Cloud Native Generative AI with Azure OpenAI Service
- Defining the Knowledge Scope of Azure OpenAI Service-Enabled Apps
- Generative AI Modeling with Azure OpenAI Service
- Azure OpenAI Service Building Blocks
- Potential Implementation Approaches
- Approach Comparison and Final Recommendation
- AI Performance Evaluation Methods
- Conclusion
- Chapter 4. Additional Cloud and AI Capabilities
- Plug-ins
- LLM Development, Orchestration, and Integration
- LangChain
- Semantic Kernel
- LlamaIndex
- Bot Framework
- Power Platform, Microsoft Copilot, and AI Builder
- Databases and Vector Stores
- Vector Search from Azure AI Search
- Vector Search from Cosmos DB
- Azure Databricks Vector Search
- Redis Databases on Azure
- Other Relevant Databases (Including Open Source)
- Additional Microsoft Building Blocks for Generative AI
- Azure AI Document Intelligence (formerly Azure Form Recognizer) for OCR
- Microsoft Fabric's Lakehouse
- Microsoft Azure AI Speech
- Microsoft Azure API Management
- Ongoing Microsoft Open Source and Research Projects
- Conclusion
- Chapter 5. Operationalizing Generative AI Implementations
- The Art of Prompt Engineering
- Generative AI and LLMOps
- Prompt Flow and Azure ML
- Securing LLMs
- Managing Privacy and Compliance
- Responsible AI and New Regulations
- Relevant Regulatory Context for Generative AI Systems
- Company-Level AI Governance Resources
- Technical-Level Responsible AI Tools
- Conclusion
- Chapter 6. Elaborating Generative AI Business Cases
- Premortem, or What to Consider Before Implementing a Generative AI Project
- Defining Implementation Approach, Resources, and Project Roadmap
- Defining Project Workstreams
- Identifying Required Resources
- Estimating Duration and Effort
- Creating a "Living" Roadmap
- Creating Usage Scenarios
- Calculating Cost and Potential ROI
- Conclusion
- Chapter 7. Exploring the Big Picture
- What's Next? The Evolution Toward Microsoft Copilot
- Expert Insights for the Generative AI Era
- David Carmona: AI Adoption and the Future of Generative AI
- Brendan Burns: The Role of Cloud Native for Generative AI Developments
- John Maeda: About AI Design and Orchestration
- Sarah Bird: Responsible AI for LLMs and Generative AI
- Tim Ward: The Impact of Data Quality on LLM Implementations
- Seth Juarez: From Generative AI Models to a Full LLM Platform
- Saurabh Tiwary: The New Microsoft Copilot Era
- Conclusion
- Appendix. Other Learning Resources
- Relevant O'Reilly Books for Your Upskilling Journey
- Other Resources and Repositories
- Index
- About the Author
- Colophon
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