LLM Engineer's Handbook: Master The Art Of Engineering Large Language Models From Concept To Production

LLM Engineer's Handbook: Master The Art Of Engineering Large Language Models From Concept To Production (Paperback, Paul Iusztin, Maxime Labonne)

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LLM Engineer's Handbook: Master The Art Of Engineering Large Language Models From Concept To Production (Paperback, Paul Iusztin, Maxime Labonne)

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    Highlights
    • Author: Paul Iusztin, Maxime Labonne
    • 522 Pages
    • Language: English
    • Publisher: Packt Publishing
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  • Description
    LLM Engineer’s Handbook by Paul Iusztin and Maxime Labonne is a practical guide to engineering large language models from concept to production. The book covers essential principles of LLM design, training, evaluation, deployment, and optimization. With real-world engineering practices and industry insights, this book is ideal for machine learning engineers, AI developers, and data scientists looking to build scalable and production-ready LLM systems.
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    In The Box
    Sales Package
    • 1 Book
    General
    Book
    • LLM Engineer's Handbook: Master The Art Of Engineering Large Language Models From Concept To Production
    Author
    • Paul Iusztin, Maxime Labonne
    Binding
    • Paperback
    Publishing Date
    • 2024
    Publisher
    • Packt Publishing
    Edition
    • Standard
    Series
    • LLM Engineer's Handbook
    Number of Pages
    • 522
    Language
    • English
    Subject
    • LLM Engineer's Handbook
    From Language
    • English
    To Language
    • English
    Tags
    • LLM, Artificial Intelligence, Machine Learning, Generative AI, AI Engineering
    Table of Content
    • Implement robust data pipelines and manage LLM training cycles
    Age Group
    • 15 Years & Above
    Specialization
    • LLM Engineer's Handbook
    Source Type
    • LLM Engineer's Handbook
    Genre
    • Academic and Professional
    Book Subcategory
    • Computing and Information Technology Books
    Degree/Diploma
    • Degree
    Author Info
    • 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 a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.
    University/Subject
    • LLM Engineer's Handbook
    Net Quantity
    • 1
    Additional Features
    Key Features
    • End-to-end guide for LLM engineering, Covers LLMs from concept to production, Practical focus on building, training & deploying models, Includes insights from industry leaders, Ideal for ML engineers & AI practitioners
    Dimensions
    Width
    • 8 inch
    Length/Height
    • 0.68 inch
    Depth
    • 9.75 inch
    Weight
    • 800 g
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