DataTalksClub / mlops-zoomcamp

Free MLOps course from DataTalks.Club

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MLOps Zoomcamp

Our MLOps Zoomcamp course

Taking the course

2024 Cohort

Self-paced mode

All the materials of the course are freely available, so that you can take the course at your own pace

  • Follow the suggested syllabus (see below) week by week
  • You don't need to fill in the registration form. Just start watching the videos and join Slack
  • Check FAQ if you have problems
  • If you can't find a solution to your problem in FAQ, ask for help in Slack



Teach practical aspects of productionizing ML services — from training and experimenting to model deployment and monitoring.

Target audience

Data scientists and ML engineers. Also software engineers and data engineers interested in learning about putting ML in production.


  • Python
  • Docker
  • Being comfortable with command line
  • Prior exposure to machine learning (at work or from other courses, e.g. from ML Zoomcamp)
  • Prior programming experience (at least 1+ year)

Asking for help in Slack

The best way to get support is to use DataTalks.Club's Slack. Join the #course-mlops-zoomcamp channel.

To make discussions in Slack more organized:


  • What is MLOps
  • MLOps maturity model
  • Running example: NY Taxi trips dataset
  • Why do we need MLOps
  • Course overview
  • Environment preparation
  • Homework

More details

  • Experiment tracking intro
  • Getting started with MLflow
  • Experiment tracking with MLflow
  • Saving and loading models with MLflow
  • Model registry
  • MLflow in practice
  • Homework

More details

  • Workflow orchestration
  • Mage

More details

  • Three ways of model deployment: Online (web and streaming) and offline (batch)
  • Web service: model deployment with Flask
  • Streaming: consuming events with AWS Kinesis and Lambda
  • Batch: scoring data offline
  • Homework

More details

  • Monitoring ML-based services
  • Monitoring web services with Prometheus, Evidently, and Grafana
  • Monitoring batch jobs with Prefect, MongoDB, and Evidently

More details

  • Testing: unit, integration
  • Python: linting and formatting
  • Pre-commit hooks and makefiles
  • CI/CD (GitHub Actions)
  • Infrastructure as code (Terraform)
  • Homework

More details

  • End-to-end project with all the things above

More details


  • Cristian Martinez
  • Tommy Dang
  • Alexey Grigorev
  • Emeli Dral
  • Sejal Vaidya

Other courses from DataTalks.Club:


I want to start preparing for the course. What can I do?

If you haven't used Flask or Docker

If you have no previous experience with ML

  • Check Module 1 from ML Zoomcamp for an overview
  • Module 3 will also be helpful if you want to learn Scikit-Learn (we'll use it in this course)
  • We'll also use XGBoost. You don't have to know it well, but if you want to learn more about it, refer to module 6 of ML Zoomcamp

I registered but haven't received an invite link. Is it normal?

Yes, we haven't automated it. You'll get a mail from us eventually, don't worry.

If you want to make sure you don't miss anything:

Supporters and partners

Thanks to the course sponsors for making it possible to create this course

Do you want to support our course and our community? Reach out to


Free MLOps course from DataTalks.Club


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