DataTalksClub/mlops-zoomcamp

Master the fundamentals of MLOps, from training and experimentation to deployment and monitoring.

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MLOps (machine learning operations) is a must-know skill for many data professionals. This free 9-week course teaches the fundamentals of MLOps, from training and experimentation to deployment and monitoring, through structured modules, hands-on workshops, and a final project. Each module introduces core MLOps concepts and tools.

This course is for data scientists, ML engineers, and software engineers who want to learn how to put machine learning models into production and operate them reliably.

To get the most out of this course, you should have prior experience with:

• Python
• Docker
• Command line basics
• Machine learning (e.g., through ML Zoomcamp)
• 1+ year of programming experience

There are two ways to follow the course: live and self-paced.

Self-paced steps:

1. Follow the materials on GitHub
2. Ask questions and share progress in Slack
3. Do the homework (self-checked) and build a project for your portfolio

• What is MLOps?
• MLOps maturity model
• NY Taxi dataset (our running example)
• Why MLOps is essential
• Course structure & environment setup
• Homework

• Introduction to experiment tracking
• MLflow basics
• Model saving and loading
• Model registry
• Hands-on MLflow exercises
• Homework

• Workflow orchestration
• Homework

• Deployment strategies: online (web, streaming) vs. offline (batch)
• Deploying with Flask (web service)
• Streaming deployment with AWS Kinesis & Lambda
• Batch scoring for offline processing
• Homework

• Monitoring ML-based services
• Web service monitoring with Prometheus, Evidently, and Grafana
• Batch job monitoring with Prefect, MongoDB, and Evidently
• Homework

• Unit and integration testing
• Linting, formatting, and pre-commit hooks
• CI/CD with GitHub Actions
• Infrastructure as Code (Terraform)
• Homework

The final project integrates everything covered in the course into an end-to-end MLOps pipeline.

Certificates are awarded to learners who complete the final project during a live cohort. After the project is graded, the certificate appears on your course platform dashboard once the instructors issue it, and the release is announced in Slack and Telegram. See the certificate guide for how to get it and add it to LinkedIn.

• Cristian Martinez
• Alexey Grigorev
• Emeli Dral

Join the #course-mlops-zoomcamp channel on DataTalks.Club Slack for discussions, troubleshooting, and networking.

To keep discussions organized:

• Follow our guidelines when posting questions.
• Review the community guidelines.

Share your progress as you go — see the learning in public guide.

Interested in supporting our community? Reach out to alexey@datatalks.club.

A few common questions. For everything else, see the full MLOps Zoomcamp FAQ.

Q: Is this course really free?
A: Yes. All videos, materials, and homework are free and open-source.

Q: Do I need prior experience?
A: Yes, some. You should know Python, Docker basics, and the machine learning workflow (for example from ML Zoomcamp), plus about a year of programming experience.

Q: Is there a live cohort?
A: Not currently. The course is fully available for self-paced study. Register on the signup form to be notified if we run a live cohort again.

Q: What does "live cohort" mean? Are there live classes?
A: No mandatory live classes. All lectures are pre-recorded. "Live" means deadlines, scored homework, peer review, and certificate eligibility.

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