MLOps — resources
roadmap.sh: https://roadmap.sh/mlops
Books
- Designing Machine Learning Systems (Chip Huyen) — the canonical text on building production ML: data, feature engineering, deployment, monitoring, and the full system view.
- Machine Learning Engineering (Andriy Burkov) — practical, end-to-end coverage of the ML lifecycle from data collection to serving and maintenance.
- Introducing MLOps (Mark Treveil et al., Dataiku) — focused introduction to MLOps concepts, governance, and operationalizing models at scale.
- Building Machine Learning Powered Applications (Emmanuel Ameisen) — hands-on guide to taking an ML idea from prototype to a deployed, iterated product.
Courses / practice
- Made With ML — free, comprehensive MLOps course covering design, development, and production with hands-on code.
- Full Stack Deep Learning — project-driven course on building and deploying real-world ML systems.
- MLOps Specialization (DeepLearning.AI) — Andrew Ng’s ML in Production specialization: pipelines, deployment, monitoring, CI/CD.