# Module 5: ML pipelines validation and testing

In previous modules, we covered what ML monitoring is, which metrics and tests to use, and what to consider in ML monitoring design. Now, let’s get to practice! This is a code-focused module.

We will apply the learnings and **implement data and model quality tests as part of a pipeline**. If you deal with batch models, such test-based monitoring can often cover all your needs. For online models, this can be a part of your setup. You can run batch checks when you get labeled data or retain the models.

We will go through an **end-to-end pipeline using a toy dataset**. We will train a model and design tests for data and model quality using Evidently. We will also explore how to automate the data pipeline testing using tools like Airflow, Prefect, and Mlflow.


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