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Building AI Intensive Python Applications

You're reading from   Building AI Intensive Python Applications Create intelligent apps with LLMs and vector databases

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836207252
Length 298 pages
Edition 1st Edition
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Generative AI FREE CHAPTER 2. Chapter 2: Building Blocks of Intelligent Applications 3. Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4. Chapter 3: Large Language Models 5. Chapter 4: Embedding Models 6. Chapter 5: Vector Databases 7. Chapter 6: AI/ML Application Design 8. Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9. Chapter 7: Useful Frameworks, Libraries, and APIs 10. Chapter 8: Implementing Vector Search in AI Applications 11. Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12. Chapter 9: LLM Output Evaluation 13. Chapter 10: Refining the Semantic Data Model to Improve Accuracy 14. Chapter 11: Common Failures of Generative AI 15. Chapter 12: Correcting and Optimizing Your Generative AI Application 16. Other Books You May Enjoy Appendix: Further Reading: Index

Data modeling

This section delves into the diverse types of data required by AI/ML systems, including structured, unstructured, and semi-structured data, and how these are applied to MDN’s news articles. The following are short descriptions of each to set a basic understanding:

  • Structured data conforms to a predefined schema and is traditionally stored in relational databases for transactional information. It powers systems of engagement and intelligence.
  • Unstructured data includes binary assets, such as PDFs, images, videos, and others. Object stores such as Amazon S3 allow storing these under a flexible directory structure at a lower cost.
  • Semi-structured data, such as JSON documents, allow each document to define its schema, accommodating both common and unique data points, or even the absence of some data.

MDN will store news articles, subscriber profiles, billing information, and more. For simplicity, in this chapter, you will focus on the data about...

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