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


ML observability course: welcome video

Welcome to the Open-source ML observability course!

How to participate?

  • Learn at your own pace. We published all 40 lessons with videos, course notes, and code examples.

  • Join the course cohort. To submit assignments and earn a certificate of completion, you must enroll in the course cohort. Sign up to save your seat and be notified when the next cohort starts.

  • Newsletter. Sign up to receive course updates and be notified when the next cohort starts.

  • Discord community. Join the community to ask questions and chat with others.

  • Code examples. Are published in this GitHub repository.

  • YouTube playlist. Subscribe to the course YouTube playlist.

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.

Module 1: IntroductionModule 2: ML monitoring metricsModule 3: ML monitoring for unstructured dataModule 4: Designing effective ML monitoringModule 5: ML pipelines validation and testingModule 6: Deploying an ML monitoring dashboard

Course calendar and deadlines for the 2023 cohort

The 2023 cohort has completed. You can learn at your own pace or sign up for the next cohort.

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!

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