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Welcome!

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

Welcome!

ML observability course: welcome video​
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.
Links
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.
Module 1: Introduction
Module 2: ML monitoring metrics
Module 3: ML monitoring for unstructured data
Module 4: Designing effective ML monitoring
Module 5: ML pipelines validation and testing
Module 6: Deploying an ML monitoring dashboard
Course calendar and deadlines
We will publish new materials throughout the course.
Module
Week
​Module 1: Introduction to ML monitoring and observability​
October 16, 2023
​Module 2: ML monitoring metrics: model quality, data quality, data drift​
October 23, 2023
​Module 3: ML monitoring for unstructured data: NLP, LLM and embeddings​
October 30, 2023
​Module 4: Designing effective ML monitoring​
November 6, 2023
​Module 5: ML pipelines validation and testing​
November 13, 2023
​Module 6: Deploying an ML monitoring dashboard​
November 20, 2023
Final assignment
November 27, 2023

Quizzes and assignment due December 1, 2023
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.
Prerequisites
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!

Module	Week
Module 1: Introduction to ML monitoring and observability	October 16, 2023
Module 2: ML monitoring metrics: model quality, data quality, data drift	October 23, 2023
Module 3: ML monitoring for unstructured data: NLP, LLM and embeddings	October 30, 2023
Module 4: Designing effective ML monitoring	November 6, 2023
Module 5: ML pipelines validation and testing	November 13, 2023
Module 6: Deploying an ML monitoring dashboard	November 20, 2023
Final assignment	November 27, 2023 Quizzes and assignment due December 1, 2023

Next - ML OBSERVABILITY COURSE

Module 1: Introduction

Last modified 21d ago