Building AI Intensive Python Applications (eBook)

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.

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Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI appsKey FeaturesGet to grips with the fundamentals of LLMs, vector databases, and Python frameworksImplement effective retrieval-augmented generation strategies with MongoDB AtlasOptimize AI models for performance and accuracy with model compression and deployment optimizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe 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.What you will learnUnderstand the architecture and components of the generative AI stackExplore the role of vector databases in enhancing AI applicationsMaster Python frameworks for AI developmentImplement Vector Search in AI applicationsFind out how to effectively evaluate LLM outputOvercome common failures and challenges in AI developmentWho this book is forThis book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.]]>