Home > Categories > Artificial Intelligence > Building AI-Intensive Python Applications
Building AI-Intensive Python Applications
đź“Ą Download PDF

Building AI-Intensive Python Applications

by Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmo

Artificial Intelligence

Book Details

Book Title

Building AI-Intensive Python Applications

Author

Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmo

Publisher

Packt Publishing Pvt. Ltd

Publication Date

2024

ISBN

9781835886762

Number of Pages

288

Language

English

Format

PDF

File Size

8.68MB

Subject

Artificial Intelligence

Table of Contents

  • Preface
  • 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
  • Other Books You May Enjoy