Name: Building AI Intensive Python Applications
Author: Rachelle Palmer Ben Perlmutter Ashwin Gangadhar Nicholas Larew Sigfrido Narváez Thomas Rueckstiess Henry Weller Richmond Alake Shubham Ranjan
ISBN: 9781836207245

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Building AI Intensive Python ApplicationsRachelle Palmer Ben Perlmutter Ashwin Gangadhar Nicholas Larew Sigfrido Narváez Thomas Rueckstiess Henry Weller Richmond Alake Shubham Ranjan

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Building AI Intensive Python Applications

Rachelle Palmer Ben Perlmutter Ashwin Gangadhar Nicholas Larew Sigfrido Narváez Thomas Rueckstiess Henry Weller Richmond Alake Shubham Ranjan

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Contributors
About the authors
About the reviewers
Note from Author
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Download a free PDF copy of this book
Chapter 1 Getting Started with Generative AI
Technical requirements
Defining the terminology
The generative AI stack
Important features of generative AI
Summary
Chapter 2 Building Blocks of Intelligent Applications
Technical requirements
Defining intelligent applications
LLMs – reasoning engines for intelligent apps
Embedding models and vector databases – semantic long-term memory
Your (soon-to-be) intelligent app
Summary
Part 1 Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
Chapter 3 Large Language Models
Technical requirements
Probabilistic framework
Machine learning for language modelling
ANNs for natural language processing
Dealing with sequential data
LLMs in practice
Summary
Chapter 4 Embedding Models
Technical requirements
What is an embedding model?
Choosing embedding models
Best practices
Summary
Chapter 5 Vector Databases
Technical requirements
What is a vector embedding?
Graph connectivity
The need for vector databases
Case studies and real-world applications
Vector search best practices
Summary
Chapter 6 AI/ML Application Design
Technical requirements
Data modeling
Data storage
Data flow
Freshness and retention
Security and RBAC
Best practices for AI/ML application design
Summary
Part 2 Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
Chapter 7 Useful Frameworks, Libraries, and APIs
Technical requirements
Python for AI/ML
AI/ML frameworks
Key Python libraries
AI/ML APIs
Summary
Chapter 8 Implementing Vector Search in AI Applications
Technical requirements
Information retrieval with MongoDB Atlas Vector Search
Building RAG architecture systems
Summary
Part 3 Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Chapter 9 LLM Output Evaluation
Technical requirements
What is LLM evaluation?
Model benchmarking
Evaluation metrics
Summary
Chapter 10 Refining the Semantic Data Model to Improve Accuracy
Technical requirements
Embeddings
Embedding metadata
Optimizing retrieval-augmented generation
Summary
Chapter 11 Common Failures of Generative AI
Technical requirements
Hallucinations
Sycophancy
Data leakage
Cost
Performance issues in generative AI applications
Summary
Chapter 12 Correcting and Optimizing Your Generative AI Application
Technical requirements
Baselining
Testing and red teaming
Information post-processing
Other remedies
Summary
Appendix: Further Reading
Index
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Building AI Intensive Python Applications