Machine Learning Engineering Open Book Github

This is an open collection of methodologies, tools and step by step instructions to help with successful training and fine-tuning of large language models and multi-modal models and their inference. This is a technical material suitable for LLM/VLM training engineers and operators. That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs. This repo is an ongoing brain dump of my experiences training Large Language Models (LLM) (and VLMs); a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B... I've been compiling this information mostly for myself so that I could quickly find solutions I have already researched in the past and which have worked, but as usual I'm happy to share these... The AI Battlefield Engineering - what you need to know in order to succeed.

There was an error while loading. Please reload this page. This repository powers MLSysBook.org, the official hub for the Machine Learning Systems textbook and its growing ecosystem of open-source tools, labs, and educational resources. MLSysBook began as a tinyML course at Harvard University by Vijay Janapa Reddi. Today, it's a global movement thanks to the many amazing people who make AI systems engineering education accessible, hands-on, and community-powered. This site exists solely to connect learners, educators, and contributors to the comprehensive ecosystem that trains the next generation of AI engineers to think holistically across algorithms, data, hardware, and infrastructure.

This repository acts as the centralized hub that ties together all parts of the MLSysBook project and ecosystem: This organization brings the textbook to life with practical, open-source resources for teaching and learning machine learning systems: This is a curated collection of free Machine Learning related eBooks available on the Internet. Please feel free to share and learn. You may visit Free-Deep-Learning-Books for Deep Learning books. If you want to contribute to this list, send a pull request.

All contributors will be recognized and appreciated. Disclaimer: The contributor(s) cannot be held responsible for any misuse of the data. You can find all the books listed below in book folder of this repo: This is a collection of free e-books about Artificial Intelligence(Machine Learning, Planning) and Data Science etc. . ML, ebook, ebooks, books, book, machine learning, statistics, free

Pattern Recognition and Machine Learning Deep Learning - Foundations as Concepts 2023 - Chris Bishop Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular. 😎 A curated list of awesome MLOps tools

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴 Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng Frouros: an open-source Python library for drift detection in machine learning systems. Principles and Practices of Engineering Artificially Intelligent Systems Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML) systems. This textbook bridges the gap between theoretical foundations and practical engineering, emphasizing the systems perspective required to build effective AI solutions.

Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader context in which ML systems operate, including data engineering, model optimization, hardware-aware training, and inference acceleration. Readers will develop the ability to reason about ML system architectures and apply enduring engineering principles for building flexible, efficient, and robust machine learning systems. Our 2025 Goal: Reach 10,000 GitHub stars and spread this resource worldwide. Sponsors like the EDGE AI Foundation match every star with funding that supports learning. New! We just started an Open Collective.

Learn more → The Problem: Students learn to train AI models, but few understand how to build the systems that actually make them work in production. When ML systems concepts are taught, students often learn individual components without grasping the holistic architecture—they can see the trees but miss the forest. If you're building ML systems in production, you know the gap between theory and real-world engineering can feel massive. That's where the Machine Learning Engineering Open Book comes in—a free, community-driven resource packed with practical knowledge for deploying ML at scale. Created by Stas Bekman, this open-source book (hosted on GitHub) covers the gritty details of ML engineering that most tutorials skip.

Think distributed training, debugging hanging PyTorch processes, GPU memory optimization, and infrastructure design—all with real code snippets and battle-tested advice. This isn’t just another "ML 101" guide. It’s the kind of resource you’ll bookmark for those "oh crap" moments when your 8-GPU training job hangs at 90%. Whether you’re debugging NCCL timeouts or designing a model-serving pipeline, there’s likely a section here that’ll save you hours. For more projects like this, follow @githubprojects. Subscribe to our newsletter to get the latest updates on open-source projects.

This is not a model but a container to hold the PDF version of the Machine Learning Engineering Open Book that you can find at https://github.com/stas00/ml-engineering From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.

This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Packed with real-world examples that take you from start to finish. This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference. Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori. This book is a general introduction to machine learning.

It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

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