DataTalksClub/mlops-zoomcamp

Project overview

A free, open-source 9-week course that teaches MLOps fundamentals through structured modules, workshops, and a final project. The curriculum covers experiment tracking, workflow orchestration, model deployment, and monitoring.

Project type

Workflow · Model Runtime · Infrastructure

Use cases

Learning & Education

Deployment

Refer to project documentation

License

License pending

Best for

• Data teams, AI engineers, and developers with one or more years of programming experience and prior knowledge of Python, Docker, and command line basics.

Key capabilities

• Introduces experiment tracking, MLflow basics, model saving/loading, and model registry.
• Teaches workflow orchestration for ML pipelines.
• Covers deployment strategies (online/batch), deploying with Flask, and streaming deployment with AWS Kinesis & Lambda.
• Monitoring ML-based services with Prometheus, Evidently, Grafana, Prefect, and MongoDB.
• Covers testing, linting, formatting, pre-commit hooks, CI/CD, and Infrastructure as Code (Terraform).
• Integrates course concepts into an end-to-end MLOps pipeline project.

Limitations and risks

• No live cohorts are currently scheduled for 2026.
• Requires prior knowledge of Python, Docker, command line basics, and 1+ year of programming experience.

Getting started

• Follow the materials on GitHub, ask questions and share progress in Slack, and do the homework (self-checked) to build a project for your portfolio.

Evidence and sources

• GitHub project description: Free MLOps course from DataTalks.Club
• README: MLOps Zoomcamp: A Free 9-Week Course on Productionizing ML Services
• README: 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 fi…
• README: 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](https://github.c…
• README: - Introduction to experiment tracking - MLflow basics - Model saving and loading - Model registry - Hands-on MLflow exercises - Homework

Reviewed at: 2026-07-17T14:44:27Z