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OtherAround three months part-time, fully self-paced with all materials free·Free

MLOps Zoomcamp (DataTalks.Club)

4.5

One of the best free routes into the production side of machine learning, the part most courses skip. It is hands-on from the start, opinionated about real tools, and the final project gives you something genuine to show. The trade-off is that it is self-paced now rather than a live cohort, so you supply the discipline.

What We Liked

  • Completely free and open source, including all videos and homework
  • Fills the production gap that most model-building courses ignore entirely
  • Teaches real tooling: MLflow, Docker, orchestration, Evidently, Grafana
  • The end-to-end project is portfolio-worthy if you actually finish it

What Could Be Better

  • Assumes you can already build a model and are comfortable with Python and Docker
  • No live 2026 cohort planned, so motivation and deadlines are on you
  • Self-paced means no graded feedback beyond the community and peer review
  • Tooling is opinionated and AWS-leaning, some choices you may swap in practice

Detailed review

Most machine learning education stops at the moment a model works in a notebook, which is exactly the moment the hard part begins, and MLOps Zoomcamp is the best free attempt I have seen at teaching that hard part. Alexey Grigorev and the DataTalks.Club team built it the same way they built ML Zoomcamp, as a free, open, project-driven course that you can follow at the original cadence or binge at your own pace, and the whole thing lives on GitHub with the videos on YouTube. It runs through six modules plus a capstone, starting with experiment tracking and model registry in MLflow, moving into pipeline orchestration, then deployment in batch, online, and streaming forms, then monitoring with Evidently and Grafana, and finally testing and CI/CD, before pulling everything together into an end-to-end project. That arc is the value.

By the end you have actually deployed and monitored something rather than just trained it, which is the skill employers are quietly desperate for and most candidates do not have. The tooling choices are practical and current, and I appreciate that the course commits to specific tools and shows you the real workflow rather than staying abstract. The honest caveats are about prerequisites and self-discipline. This is not where you learn machine learning, it assumes you can already build a model, write comfortable Python, and find your way around the command line and Docker, so a beginner should do something like ML Zoomcamp or Andrew Ng's specialisation first and then come here.

The team has said they are not planning a live cohort in 2026, and that matters more than it sounds, because the live version's deadlines, leaderboard, and peer-reviewed project pushed a lot of people across the finish line. Self-paced, the same material is all there, but the accountability is gone and you have to bring your own. The tooling is also opinionated and leans on AWS in places, so some of the specific choices will differ from whatever your eventual employer uses, though the concepts transfer cleanly. My take is that for the right learner this is one of the highest-leverage free courses in the whole field, the person who can build models but has never shipped one.

Do the project for real, do not just watch the videos, because the project is where the learning actually happens and the thing you can point a hiring manager to afterwards.

[ final ]

The verdict.

Close to essential for anyone who can train a model but has never deployed or maintained one. If you are still learning the modelling fundamentals, do ML Zoomcamp or Andrew Ng first, then come here to learn the half of the job nobody teaches.