MLOps Zoomcamp: A Free 9-Week Course on Productionizing Machine Learning
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
At a glance
- What is it?
- MLOps Zoomcamp by DataTalks.Club is a free, self-paced 9-week course teaching data scientists and ML engineers how to move models from experimentation into production. No live cohort is planned for 2026; all materials are available on GitHub and YouTube.
- Who is it for?
- MLOps Zoomcamp is a solid choice for a data scientist or ML engineer who already knows Python and Docker and wants a structured path through experiment tracking, deployment, monitoring, and CI/CD practices. It is not the right starting point for someone without prior ML or programming experience: the README lists Python, Docker, command line basics, and a machine learning background as prerequisites, with roughly a year of programming experience expected.
- Can I use it commercially?
- Not without permission. GitHub finds no licence file in the repository, and without a licence all rights are reserved by default: you may read the code but not reuse it. Check the README, or ask the authors, before using it.
- Is it still maintained?
- Yes. The repository last received commits 15 days ago.
- What is it written in?
- Mainly Jupyter Notebook, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 30, 2026, and from our analysis. They are not legal advice.
Editorial analysis
What MLOps Zoomcamp Teaches and Who It Is For
MLOps (machine learning operations) covers the practices and tools needed to move a trained model from a notebook into a production system that can be deployed, monitored, and maintained over time. MLOps Zoomcamp is a free 9-week course designed around this problem. It targets data scientists, ML engineers, and software engineers who want to operationalize machine learning models rather than just build them.
The README states the prerequisites plainly: Python, Docker, command line basics, prior machine learning experience (the companion ML Zoomcamp is suggested), and roughly one year of programming experience. The course is not introductory. It assumes you already know how to train a model and want to learn what happens next.
How the Course Is Organized
The repository is divided into numbered module directories: 01-intro through 06-best-practices, plus 07-project for the final capstone. Each module directory contains Jupyter notebooks, scripts, configuration files, and homework assignments. Videos for each module are in a YouTube playlist linked from the README.
The modules follow a natural production pipeline sequence. Module 1 introduces MLOps concepts and the maturity model. Module 2 covers experiment tracking with MLflow, including model saving, loading, and the MLflow model registry. Module 3 covers workflow orchestration. Module 4 covers deployment in three modes: online web services with Flask, streaming deployment with AWS Kinesis and Lambda, and batch scoring for offline processing. Module 5 covers monitoring ML-based services using Prometheus, Evidently, and Grafana for web services, and Prefect, MongoDB, and Evidently for batch jobs. Module 6 covers engineering best practices: unit and integration testing, linting, formatting, pre-commit hooks, CI/CD with GitHub Actions, and infrastructure as code with Terraform.
The final project in 07-project/ requires integrating all of these elements into a complete end-to-end MLOps pipeline.
Self-Paced Study: How to Follow the Course Without a Cohort
The README documents two modes: live cohort and self-paced. No live cohort is planned for 2026. The note in the README reads: "We don't plan to run a live cohort in 2026. The course is fully available for self-paced study now."
For self-paced study, the steps are: 1. Follow the materials on GitHub 2. Ask questions and share progress in the DataTalks.Club Slack channel (#course-mlops-zoomcamp) 3. Do the homework (self-checked) and build a project for a portfolio
The homework is available but not graded in self-paced mode. The leaderboard and peer review features are only active during a live cohort. Certificates are issued only to learners who complete the final project during a live cohort; self-paced learners cannot earn a certificate from the current available run.
To access the course materials, clone the repository and navigate to the relevant module directory. The README links to a YouTube playlist for all video lectures. No installation of a dedicated platform is required; the GitHub repository and YouTube together constitute the full course delivery.
Tools and Technologies Covered in Each Module
Module 2 centers on MLflow for experiment tracking and the model registry. Module 4 uses Flask for web service deployment, AWS Kinesis and Lambda for streaming, and Python batch scripts for offline scoring. Module 5 uses Prometheus for metrics collection, Evidently for data drift detection, Grafana for dashboards, Prefect for batch job orchestration, and MongoDB for batch job monitoring. Module 6 introduces GitHub Actions for CI/CD pipelines and Terraform for infrastructure as code.
The README does not document which cloud provider is required for the AWS sections, but Kinesis and Lambda are AWS-specific services. The Terraform module implies some familiarity with IaC tooling. Learners working in environments without AWS access may need to adapt the streaming and Lambda exercises.
The running dataset used throughout the course is the NY Taxi dataset, introduced in Module 1. Using the same dataset across all modules lets learners track a model through the full production lifecycle without switching contexts.
What the Course Does Not Cover
MLOps Zoomcamp is focused on the MLOps engineering layer: tracking experiments, deploying models, and monitoring them in production. It does not cover advanced ML modeling, feature engineering, or deep learning architectures. The ML skills are assumed as a prerequisite, not taught.
The course is also specific in its tool choices. MLflow, Prefect, Evidently, Grafana, Prometheus, Flask, AWS Kinesis, Lambda, and Terraform are the named technologies. The course does not cover Kubeflow, Vertex AI, SageMaker pipelines, Azure ML, or other managed MLOps platforms. Engineers working in managed cloud environments where those services are the standard choice will need to map the concepts taught here onto their platform manually.
The README also does not document a path for learners who want to continue to more advanced MLOps topics after completing the course. The DataTalks.Club community on Slack is the suggested channel for continued learning and networking.
MLOps Zoomcamp Versus a Formal MLOps Certificate Program
Several universities and online platforms offer paid MLOps and ML engineering certificate programs. The main difference from MLOps Zoomcamp is cost and structure. MLOps Zoomcamp is free and open source, with all materials in the GitHub repository and on YouTube.
The trade-off is support: paid programs often include live instructor sessions, graded assignments with feedback, and a certificate that carries an institutional name. MLOps Zoomcamp's certificate, when available during a live cohort, comes from DataTalks.Club and is added to LinkedIn through the course platform dashboard. No 2026 cohort is planned, so the certificate path is currently unavailable.
For learners whose primary goal is job-market credentials rather than skill acquisition, the lack of a current live cohort is a concrete limitation. For learners who want hands-on practice with a specific and well-documented open source toolchain, the course covers more practical ground per hour than most paid alternatives, based on the module contents documented in the README.
Maintenance and Repository Activity
The last push to the repository was on 2026-09-15. The repository has no GitHub releases, consistent with its nature as a course material repository rather than a versioned software package. The license is not documented in the repository metadata; the README does not specify a license.
The course materials live in numbered module directories, a cohorts/ directory (which presumably contains cohort-specific information), and a generate/ directory. The README mentions a course platform at courses.datatalks.club for deadline tracking and homework submission during live cohorts.
The community infrastructure is the DataTalks.Club Slack workspace, specifically the #course-mlops-zoomcamp channel. The README links to community guidelines and instructions for posting questions, suggesting that the Slack channel is actively moderated. Telegram is used for announcements.
Editorial conclusion
MLOps Zoomcamp is a solid choice for a data scientist or ML engineer who already knows Python and Docker and wants a structured path through experiment tracking, deployment, monitoring, and CI/CD practices. It is not the right starting point for someone without prior ML or programming experience: the README lists Python, Docker, command line basics, and a machine learning background as prerequisites, with roughly a year of programming experience expected. Certificates are only issued during live cohorts, and no cohort is planned for 2026. Self-paced learners can work through all seven modules and build a portfolio project, but the certificate and graded leaderboard are unavailable without a live run.
Frequently asked questions
Is there a free full course on MLOps available?
Yes. MLOps Zoomcamp by DataTalks.Club is free and fully available for self-paced study on GitHub. All video lectures are on YouTube and all materials including homework are in the repository.
What exactly is MLOps?
MLOps is the set of practices and tools for deploying, monitoring, and operating machine learning models in production. The course README describes it as covering everything from training and experimentation through deployment and monitoring.
Does MLOps have a future as a career skill?
The README describes MLOps as a must-know skill for many data professionals. The course itself does not make claims about career trajectories or salaries; it focuses on the practical toolchain.
Official sources
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