Applied AI for Enterprise Java Development

Table of contents

1. Brief Table of Contents (Not Yet Final)
2. Preface
   1. Why We Wrote the Book
   2. Who Should Read This Book
   3. How the Book Is Organized
   4. Prerequisites and Software
3. 1. The Enterprise AI Conundrum
   1. Understanding the AI Landscape: A Technical Perspective all the way to Gen AI
      1. Machine Learning (ML): The Foundation of today’s AI
      2. Deep Learning: A Powerful Tool in the AI Arsenal
      3. Generative AI: The Future of Content Generation
   2. Open-Source Models and Training Data
      1. Why Open Source is an Important Driver for Gen AI
      2. Open Source Training Data
      3. Adding Company specific Data to LLMs
      4. Explainable and transparent AI decisions
   3. Ethical and Sustainability Considerations
   4. The lifecycle of LLMs and ways to influence their behaviour
   5. MLOps vs DevOps
   6. Conclusion
4. 2. Inference API
   1. What is an Inference API?
      1. Examples of Inference APIs
   2. Deplying Inference Models in Java
      1. Inferencing models with DJL
      2. Under the hood
      3. Inferencing Models with gRPC
   3. Next Steps
5. 3. Accessing the Inference Model with Java
   1. Connecting to an Inference API with Quarkus
      1. Architecture
      2. The Fraud Inference API
      3. Creating the Quarkus project
      4. REST Client interface
      5. REST Resource
      6. Testing the example
   2. Connecting to an inference API with Spring Boot WebClient
      1. Adding WebClient Dependency
      2. Using the WebClient
   3. Connecting to the Inference API with Quarkus gRPC client
      1. Adding gRPC Dependencies
      2. Implementing the gRPC Client
   4. Going Beyond
6. About the Authors