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⇱ Automate and Evaluate ML Pipeline Tests | Coursera

Automate and Evaluate ML Pipeline Tests

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Automate and Evaluate ML Pipeline Tests

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Intermediate level

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3 hours to complete
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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

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There is 1 module in this course

Machine learning systems shift over time, making structured testing essential. In this short course, you’ll learn how to evaluate ML pipelines using unit, integration, and smoke tests and how to detect data drift across critical features. You will also create automated regression test suites that compare new model outputs to golden datasets, helping you catch degradation early and deploy reliably. Through concise videos, readings, hands-on practice, and guided coaching, you’ll define meaningful ML test cases and configure nightly pytest suites. By the end, you will have a practical, reusable testing framework you can apply directly to real-world ML pipelines.

Machine learning systems shift over time, making structured testing essential. In this short course, you’ll learn how to evaluate ML pipelines using unit, integration, and smoke tests and how to detect data drift across critical features. You will also create automated regression test suites that compare new model outputs to golden datasets, helping you catch degradation early and deploy reliably. Through concise videos, readings, hands-on practice, and guided coaching, you’ll define meaningful ML test cases and configure nightly pytest suites. By the end, you will have a practical, reusable testing framework you can apply directly to real-world ML pipelines.

What's included

7 videos3 readings3 assignments1 ungraded lab

7 videos•Total 43 minutes
  • Welcome + Why ML Tests Matter•5 minutes
  • Why ML Pipelines Fail Without Structured Tests•6 minutes
  • Designing Feature-Level Test Cases for Drift•6 minutes
  • What a Regression Suite Does•7 minutes
  • Setting Up Nightly Pytest Runs•10 minutes
  • Integrating Drift Checks Into Regression Suites•5 minutes
  • Congratulations and Continuous Learning Journey•4 minutes
3 readings•Total 28 minutes
  • Unit, Integration, Smoke Tests for ML•10 minutes
  • Output Comparison Strategies & Thresholds•10 minutes
  • Maintaining Golden Datasets•8 minutes
3 assignments•Total 40 minutes
  • Graded Quiz: Designing and Automating ML Pipeline Tests•20 minutes
  • Hands-on Activity: Build a Test Case Matrix•10 minutes
  • Hands-on Activity: Write a Basic Regression Test•10 minutes
1 ungraded lab•Total 45 minutes
  • Configure a Nightly Pytest Regression Pipeline•45 minutes

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