# Module 1: Introduction

This theoretical module introduces the key topics of machine learning monitoring and observability.

It covers the following topics:

* what can go wrong with ML models in production;
* what ML monitoring and observability are and how they fit in the ML lifecycle;
* what types of evaluation you might need, from model quality to data drift;
* key considerations to keep in mind when designing your monitoring.

At the end of this module, you will know the key concepts related to ML monitoring and observability and how they will be covered throughout the course.


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