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    LLM Engineer's Handbook. Master the art of engineering large language models from concept to production

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza Tahir
    LLM Engineer's Handbook. Master the art of engineering large language models from concept to production Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza Tahir - okladka książki

    LLM Engineer's Handbook. Master the art of engineering large language models from concept to production Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza Tahir - okladka książki

    LLM Engineer's Handbook. Master the art of engineering large language models from concept to production Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza Tahir - audiobook MP3

    LLM Engineer's Handbook. Master the art of engineering large language models from concept to production Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza Tahir - audiobook CD

    Serie wydawnicze:
    Building
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    522
    Dostępne formaty:
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    Ebook (143,10 zł najniższa cena z 30 dni)

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    Do przechowalni

    Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.
    Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.
    By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.

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    O autorach książki

    Paul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. Paul's determined passion and mission are to build data-intensive AI/ML products that serve the world and educate others about the process. As the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade ML, Paul has significantly enriched the engineering and MLOps community. His weekly content on ML engineering and his open-source courses focusing on end-to-end ML life cycles, such as Hands-on LLMs and LLM Twin, testify to his valuable contributions.
    Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.

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