MLOps Zoomcamp
Our MLOps Zoomcamp course
Taking the course
2025 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
Overview
Objective
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.
Pre-requisites
- 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:
Syllabus
We encourage Learning in Public
- 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
Instructors
- Cristian Martinez
- Tommy Dang
- Alexey Grigorev
- Emeli Dral
- Sejal Vaidya
Other courses from DataTalks.Club:
FAQ
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 run this course
Do you want to support our course and our community? Reach out to [email protected]