MLOps Zoomcamp: A Free 9-Week Course on Productionizing ML Services

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

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Quick Links

About the Course

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.

Who Should Join

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.

Prerequisites

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

How to Take the Course

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

[!NOTE] We don't plan to run a live cohort in 2026. The course is fully available for self-paced study now. Register here to be notified if we run a live cohort again.

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

Syllabus

Module 1: Introduction

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

Module 2: Experiment Tracking & Model Management

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

Module 3: Orchestration & ML Pipelines

• Workflow orchestration
• Homework

Module 4: Model Deployment

• 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

Module 5: Model Monitoring

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

Module 6: Best Practices

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

Final Project

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

Certificate

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.

Instructors

• Cristian Martinez
• Alexey Grigorev
• Emeli Dral

Community & Support

Getting Help on Slack

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.

Learning in Public

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

Sponsors

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

FAQ

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.

About DataTalks.Club

DataTalks.Club is a global online community of data enthusiasts. It's a place to discuss data, learn, share knowledge, ask and answer questions, and support each other.

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All the activity at DataTalks.Club mainly happens on Slack. We post updates there and discuss different aspects of data, career questions, and more.

At DataTalks.Club, we organize online events, community activities, and free courses. You can learn more about what we do at DataTalks.Club docs.