Ml Engineering Machine Learning Engineering Open Book

Leo Migdal

-Nov 17, 2025, 1:53 PM

ml engineering machine learning engineering open book

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.

This is an open collection of methodologies, tools and step by step instructions to help with successful training of large language models and multi-modal models. 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... Currently, I’m working on developing/training open-source Retrieval Augmented Generation (RAG) models at Contextual.AI. 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...

My apologies if the layout is a bit unstable while I’m writing new chapters and gradually re-organizing the content to be more intuitive. 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. An open Machine Learning Engineering open-book covering compute, storage, networking, training and inference best practices. Machine Learning Engineering is an open book that compiles practical knowledge for ML engineers working on large-scale training and inference systems.

It covers hardware selection (accelerators, storage), networking, distributed training strategies, inference optimizations, debugging and operational playbooks. A fully asynchronous reinforcement learning system for large reasoning and … An open-source toolkit for generating, publishing, and loading CellARC datasets … An open-source framework combining computer vision and motor-imagery EEG … 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 An open collection of methodologies to help with successful training of large language models and multi-modal models.

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 andIDEFICS-80B multi-modal... Currently, I'm working on developing/training open-source Retrieval Augmented models at Contextual.AI. 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... My apologies if the layout is a bit unstable while I'm writing new chapters and gradually re-organizing the content to be more intuitive.

ml-engineering — Comprehensive Guide for Large Language Model Training and Inference The ml-engineering project is an open-source book that serves as a comprehensive guide for machine learning engineers, particularly those working with large language models (LLMs) and multi-modal models. It offers methodologies, tools, and step-by-step instructions for training, fine-tuning, and inference. Unique to this project is its focus on practical, hands-on solutions, with scripts and commands that engineers can directly apply to their work. Topics: [, ", a, i, ", ,, , ", i, n, f, e, r, e, n, c, e, ", ,, , ", l, a, r, g, e, -, l, a, n, g, u, a,... This article is automatically generated by AI based on GitHub project information and README content analysis

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

Ml Engineering Machine Learning Engineering Open Book

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This Is An Open Collection Of Methodologies, Tools And Step

This Is An Open Collection Of Methodologies, Tools And Step

My Apologies If The Layout Is A Bit Unstable While

Our 2025 Goal: Reach 10,000 GitHub Stars And Spread This

When ML Systems Concepts Are Taught, Students Often Learn Individual