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⇱ Debug ML Code: Fix, Trace & Evaluate | Coursera

Debug ML Code: Fix, Trace & Evaluate

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Debug ML Code: Fix, Trace & Evaluate

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

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Recently updated!

March 2026

Assessments

4 assignments¹

AI Graded see disclaimer
Taught in English

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This course is part of the Gradient to Production: MLOps & Model Serving Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There is 1 module in this course

Machine learning systems fail in ways that traditional software does not—data changes, schema mismatches, and model assumptions all create unique bugs. This course teaches you how to trace, fix, and validate these issues using a structured debugging workflow. You’ll write targeted unit tests, interpret stack traces and logs, patch defects, and confirm resolutions through regression testing. Each lesson includes concise videos, practical readings, hands-on work, and a realistic ungraded lab. By the end, you’ll know how to diagnose ML failures quickly, prevent regressions, communicate your fixes clearly, and build more reliable ML codebases.

Machine learning systems fail in ways that traditional software does not—data changes, schema mismatches, and model assumptions all create unique bugs. This course teaches you how to trace, fix, and validate these issues using a structured debugging workflow. You’ll write targeted unit tests, interpret stack traces and logs, patch defects, and confirm resolutions through regression testing. Each lesson includes concise videos, practical readings, hands-on work, and a realistic ungraded lab. By the end, you’ll know how to diagnose ML failures quickly, prevent regressions, communicate your fixes clearly, and build more reliable ML codebases.

What's included

5 videos3 readings4 assignments

5 videos•Total 29 minutes
  • Welcome: How Testing Helps You Debug ML Faster•3 minutes
  • Writing Pytest Cases for ML Preprocessing Functions•10 minutes
  • Reading Stack Traces: What They Reveal About Your Pipeline•10 minutes
  • Regression Testing for ML: When Is a Fix Really Fixed?•5 minutes
  • Congratulations and Continuous Learning Journey•2 minutes
3 readings•Total 17 minutes
  • Testing ML Code: Strategies That Reveal Defects Early•5 minutes
  • Log Analysis for ML Systems: Interpreting Errors, Warnings, and Signals•6 minutes
  • Patch, Verify, Approve: The Workflow for ML Fixes•6 minutes
4 assignments•Total 54 minutes
  • Hands-On Activity: Write Unit Tests for a Feature Engineering Function•12 minutes
  • Hands-On Activity: Trace a KeyError to a Missing Feature Column•12 minutes
  • Hands-On Activity: Run a Full Test Suite and Compare Before/After Metrics•10 minutes
  • Debugging in Practice: Identify, Fix, and Validate ML Defects•20 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.