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

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Autorzy:
    Richmond Alake, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Rachelle Palmer, Ben Perlmutter, Shubham Ranjan, Thomas Rueckstiess, Henry Weller
    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 - okładka ebooka

    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 - okładka ebooka

    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 - okładka audiobooka 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 - okładka audiobooks CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    +209 pkt
    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.

    Wybrane bestsellery

    O autorach ebooka

    Ashwin Gangadhar is a Senior Solutions Architect at MongoDB with over a decade of experience in data-driven solutions for e-commerce, HR analytics, and finance. He holds a master's in Controls and Signal Processing and specializes in search relevancy, computer vision, and NLP. Passionate about continuous learning, Ashwin explores new technologies and innovative solutions. Born and raised in Bengaluru, India, he enjoys traveling, exploring cultures through cuisine, and playing the guitar.
    Nicholas Larew is a Senior Engineer on MongoDB's Education AI team. He works on MongoDB's AI chatbot, including the open-source framework that powers it, and MongoDB's content generation and dataset curation efforts. Before working in AI, Nicholas wrote and maintained documentation and sample applications for MongoDB's developer-facing products.
    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.
    Ben Perlmutter is a Senior Engineer on the Education AI team at MongoDB. He applies AI technologies such as LLMs, embedding models, and vector databases to improve MongoDB's educational experience. His team built the MongoDB AI chatbot, which uses RAG to help thousands of users a week learn about MongoDB. Ben formerly worked as a technical writer specializing in developer-focused documentation.
    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.
    Thomas Rueckstiess is a Senior Staff Research Scientist and Head of the Machine Learning Research Group at MongoDB. Thomas holds a PhD in Machine Learning, specializing in neural networks and reinforcement learning, transformers, and structured data modeling. He joined MongoDB in 2012 and was previously the Lead Engineer for MongoDB Compass and Atlas Charts.
    Henry Weller is the dedicated Product Manager for Atlas Vector Search, focusing on the query features and scalability of the service, as well as developing best practices for users. He helped launch Atlas Vector Search from Public Preview into General Availability in 2023 and continues to lead the delivery of core features for the service. Henry joined MongoDB in 2022 and was previously a data engineer and backend robotics software engineer.

    Oceny i opinie klientów: 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 (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

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