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Building AI-Intensive Python Applications. Create intelligent apps with LLMs and vector databases

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Building AI-Intensive Python Applications. Create intelligent apps with LLMs and vector databases Richmond Alake, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Rachelle Palmer, Ben Perlmutter, Shubham Ranjan, Thomas Rueckstiess, Henry Weller - okladka książki

Building AI-Intensive Python Applications. Create intelligent apps with LLMs and vector databases Richmond Alake, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Rachelle Palmer, Ben Perlmutter, Shubham Ranjan, Thomas Rueckstiess, Henry Weller - okladka książki

Building AI-Intensive Python Applications. Create intelligent apps with LLMs and vector databases Richmond Alake, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Rachelle Palmer, Ben Perlmutter, Shubham Ranjan, Thomas Rueckstiess, Henry Weller - audiobook MP3

Building AI-Intensive Python Applications. Create intelligent apps with LLMs and vector databases Richmond Alake, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Rachelle Palmer, Ben Perlmutter, Shubham Ranjan, Thomas Rueckstiess, Henry Weller - audiobook CD

Ocena:
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.

O autorach książki

Rachelle Palmer is the Product Leader for Developer Database Experience and Developer Education at MongoDB, overseeing the driver client libraries, documentation, framework integrations, and MongoDB University. She has built sample applications for MongoDB in Java, PHP, Rust, Python, Node.js, and Ruby. Rachelle joined MongoDB in 2013 and was previously the director of the technical services engineering team, creating and managing the team that provided support and CloudOps to MongoDB Atlas.
Shubham is a Product Manager at MongoDB for Python and a core contributing member to AI initiatives at MongoDB. He is also a Python developer and has published over 700 technical articles on topics ranging from data science and machine learning to competitive programming. Since joining MongoDB in 2019, Shubham has held several roles, progressing from Software Engineer to Product Manager for multiple products.

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