ML Engineering Open Book is a technical reference collection for training and deploying large language models and multimodal models at scale.
The project addresses the practical challenges of LLM and VLM training and inference by compiling methodologies, tools, and executable commands drawn from real-world experience training models like BLOOM-176B and IDEFICS-80B. It covers the full stack: hardware considerations including GPU selection and cost-benefit analysis, storage and networking infrastructure, orchestration systems like SLURM, training techniques, inference optimization, and debugging strategies. The material emphasizes copy-paste commands and scripts that practitioners can apply immediately rather than theoretical exposition.
This resource suits training engineers and ML operations specialists working on large-scale model projects who need practical solutions to infrastructure and training problems. It is most valuable for teams deploying models on multi-GPU clusters or cloud infrastructure where decisions about hardware upgrades, storage architecture, and resource orchestration directly impact project success and cost. The collection draws from specific production experiences rather than generic best practices, making it particularly relevant for those tackling similar scale challenges.
The maintainer responds to new issues and pull requests within hours, indicating active engagement with the community and willingness to address questions and contributions promptly.
