Awesome

MLOps Zoomcamp

Our MLOps Zoomcamp course

Sign up here: https://airtable.com/shrCb8y6eTbPKwSTL
Register in DataTalks.Club's Slack
Join the #course-mlops-zoomcamp channel
Tweet about the course!
Start watching course videos! Course playlist
Technical FAQ
For announcements, join our Telegram channel

Taking the course

2025 Cohort

Start: May 2025
Registration link: https://airtable.com/shrCb8y6eTbPKwSTL
Subscribe to our public Google Calendar (it works from Desktop only)

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:

Follow these recommendations when asking for help
Read the DataTalks.Club community guidelines

Syllabus

We encourage Learning in Public

Module 1: Introduction

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

More details

Module 2: Experiment tracking and model management

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

Module 3: Orchestration and ML Pipelines

Workflow orchestration
Mage

More details

Module 4: Model Deployment

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

Module 5: Model Monitoring

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

More details

Module 6: Best Practices

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

More details

Project

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

Check Module 5 from ML Zoomcamp
The section about Docker from Data Engineering Zoomcamp could also be useful

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:

Register in our Slack and join the #course-mlops-zoomcamp channel
Subscribe to our YouTube channel

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 alexey@datatalks.club