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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By : Rachelle 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

Building AI Intensive Python Applications

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

Overview of this book

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
Table of Contents (18 chapters)
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Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
8
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
11
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Appendix: Further Reading: Index

AI/ML frameworks

AI/ML frameworks are essential tools that streamline the development and deployment of ML models, providing pre-built algorithms, optimized performance, and scalable solutions. They enable developers to focus on refining their models and GenAI applications rather than getting bogged down by low-level implementations. Using frameworks ensures efficiency, adaptability, and the ability to harness cutting-edge AI advancements. Developers should be interested in these frameworks as they also reduce development time and enhance the potential for breakthroughs in GenAI.

MongoDB has integrations with many AI/ML frameworks that may be familiar to developers, such as LangChain, LlamaIndex, Haystack, Microsoft Semantic Kernel, DocArray, and Flowise.

In this section, you will learn about LangChain, one of the most popular GenAI frameworks. Although it is very popular, it is certainly not the only popular framework. If you are interested in other frameworks, you can check...

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