awesome-mlops
A curated list of references for MLOps
A curated reading list for MLOps, Machine Learning Operations, organized across 20 sections covering learning resources, tools, model deployment, monitoring, governance, and community, serving as a map into the field rather than a tool you install.
This repository is a curated reading list for MLOps, which stands for Machine Learning Operations. MLOps is the practice of building, deploying, monitoring, and maintaining machine learning models in production environments, treating AI development with the same engineering discipline applied to software.
The list is organized into roughly twenty sections covering the full lifecycle. These include foundational resources on what MLOps is and why it matters, books and online courses for people learning the field, workflow management tools, feature stores (systems for storing and sharing the data inputs models use), data engineering practices, model deployment and serving, testing and monitoring of live models, and infrastructure considerations. There are also sections on governance and responsible AI, the economics of machine learning, community forums, and newsletters.
Each section is a numbered list of links pointing to external resources: papers, blog posts, course pages, books on O'Reilly and similar platforms, open-source toolkits, and community organizations. The list does not explain the resources in depth but acts as a starting index you can browse to find what is relevant to your role or question.
The collection is maintained by Dr. Larysa Visengeriyeva and follows the 'awesome list' convention common on GitHub, where curated link collections for a specific topic are published openly for the community to use and contribute to. If you are new to machine learning in production and want to know where to look, this is a broad map of the field's learning resources rather than a tool or library you install and run.
The full README is longer than what was shown.
Where it fits
- Find books, courses, and papers to learn MLOps from scratch or fill gaps in specific areas like feature stores or model monitoring.
- Discover open-source toolkits for workflow management, model serving, and infrastructure for your machine learning team.
- Explore governance and responsible AI resources when building ML pipelines that need auditing, fairness checks, or compliance documentation.