Give us a star ⭐️ on GitHub to support the project!
Free Open-source ML observability course for data scientists and ML engineers. Join us to learn production ML monitoring.
The 2023 cohort has completed. You can learn at your own pace or sign up for the 2024 cohort.
The foundations of ML observability: from exploratory data analysis and model evaluation pre-deployment to continuous production monitoring and debugging.
The 2023 cohort of the Open-source ML observability course has completed.
Sign up to be notified when the 2024 cohort starts.
“Model drift is not a phenomenon that will be eliminated from one day to another in production level. But after that course, someone can be ready to act before critical performance/business issues are raised.”
“The course covered a wealth of practical MLOps material and featured many insightful discussions led by the course team. I highly recommend incorporating Evidently AI's open-source methodology and products into your MLOps toolbox.”
“I enjoyed the Open-source ML Observability course, gaining valuable insights into the importance of monitoring throughout the ML pipeline. The practical integration of Evidently with tools like MLflow and Airflow enriched my learning experience, enhancing my skills in ML observability.”
“The course equipped me to navigate the intricate relationship between machine learning and business goals, giving me a holistic approach to MLOps. Emeli Dral is a great teacher, highly recommended.”
Our course is organized into six modules. You can follow the complete course syllabus or pick only the modules that are most relevant to you.
-min.png)
Basics of ML monitoring and observability.
Metrics and evaluation methods for structured data.
-min.png)
Metrics and evaluation methods for unstructured data.
Key questions to consider when customizing ML monitoring for your model.
-min.png)
How to deploy an end-to-end pipeline to check data and ML model quality.
-min.png)
How to deploy an ML monitoring service and design a live dashboard.
We published all 40 lessons with videos, course notes, and code examples so you can learn at your own pace. Jump to the course website to start learning now!
Yes, the course is 100% free.
The course is organized into six modules. Each module consists of on-demand videos and practical code examples.
To 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 is ready to launch.
This is optional — you can also learn at your own pace and go through the course materials, which are publicly available.
The course lasts seven weeks. There are six modules with learning materials, and one extra week to complete the final assignment.
However, you can also learn at your own pace — whatever works best for you!
You will receive a certificate if you enroll in the course cohort and successfully complete all the assignments.
Note that the option to receive the certificate is available only to those who participate in the course cohort. The next cohort will take place in 2024. Sign up to get updates when it starts.
There are both theoretical and code-focused modules that require knowledge of Python. But no worries: we will walk you through the code! You can also skip these parts and still learn a lot.
Have a question or just want to say “Hi”? Jump to our Discord #-ml-observability-course channel to chat with fellow learners and get support from the course team.
We published all 40 lessons with videos, course notes, and code examples. Jump to the course website to start learning now!
Yes, sign up here to receive course updates and be notified when the next cohort is ready to launch.
Yes! All course materials are public so you can get back to them at your convenience, during or after the course.
In the Evidently GitHub repository, we added a special set of issues labeled “hacktoberfest."
Check out the issues we prepared for Hacktoberfest. You can pick one of them or propose a different metric.
Choose the drift method you want to implement and submit your pull request!
Wait for your pull request to be reviewed.
Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor the performance of ML models from validation to production. You can check it out on GitHub or explore the documentation.
Hacktoberfest is an annual event to celebrate open-source and encourage contributions. It runs for the 9th time this year. Eligible participants will get prizes from DigitalOcean. But first and foremost, it is a great reason to create your first (or hundredth) pull request! You can check out the complete rules here.
Evidently is an open-source project, and is always open for contributions. For Hacktoberfest, we added a special set of issues labeled “hacktoberfest” to the Evidently GitHub repository. We invite data scientists to dip their toes into open-source contribution and help us add new statistical metrics and tests to detect data drift for production ML models. Check out Hacktoberfest issues on our GitHub. Head to the Evidently Hacktoberfest guide for clear steps and detailed examples. Sign up to receive the kick-off newsletter.
Don’t forget to register for Hacktoberfest by October 31! If you register and have 4 pull requests accepted among the first 40000 participants, you can get a prize. Read more here.
