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Free Open-source ML observability course for data scientists and ML engineers by Evidently AI.


Welcome to the Open-source ML observability course!

How to participate?

  • Join the course. Sign up to receive weekly updates with course materials and information about office hours.
  • Course platform [OPTIONAL]. If you want to receive a course certificate, you should also register on the platform and complete all the assignments before December 1, 2023.
The course starts on October 16, 2023. The videos and course notes for the new modules will be released during the course cohort.
  • Discord community. Join the community to ask questions and chat with others.
  • Code examples. Will be published in this GitHub repository throughout the course.
  • YouTube playlist. Subscribe to the course YouTube playlist to keep tabs on video updates.
Enjoying the course? Star Evidently on GitHub to contribute back! This helps us create free, open-source tools and content for the community.

What the course is about

This course is a deep dive into ML model observability and monitoring.
We explore different types of evaluations, from data quality to data drift, and how this fits in the model lifecycle. We also cover the engineering aspect of ML observability and how to integrate it with your ML services and pipelines.

Course structure

ML observability course is organized into six modules. You can follow the complete course syllabus or pick only the modules that are most relevant to you.

Course calendar and deadlines

We will publish new materials throughout the course.

Our approach

  • Blend of theory and practice. The course combines key concepts of ML observability and monitoring with practice-oriented tasks.
  • Practical code examples. We provide end-to-end deployment blueprints and walk you through the code examples.
  • Focus on open-source. The course is built upon open-source tools to make ML observability accessible to all.
  • The course is free and open to everyone. All course videos are public so you can rewatch them anytime.


There are both theoretical and code-focused modules that require knowledge of Python. We will walk you through the code, but you can skip these parts and still learn a lot.

Who is it for

This course is useful to professionals who have dealt with ML models in production and those preparing to deploy ML models:
  • Data scientists,
  • ML engineers,
  • Technical product managers,
  • Analysts.
Let’s dive in!