CI and testing for ML projects
Add fast automated checks that catch broken data, code and models before they reach users
A taste of a lesson
Our CI test asserts the model's accuracy is exactly 0.912 and it fails randomly. What should I do?
Exact equality is the problem. Even with fixed seeds, training and some GPU operations vary slightly, so the score moves a little between runs. Decide what change actually matters: for example, fail only if accuracy falls more than one percentage point below the current live model on the same fixed evaluation set. Estimate normal variation by running the evaluation several times and set the tolerance just outside it. Also add checks on your critical slices, since an average can hold steady while one group gets worse. Quick exercise: how many repeated runs would you use to estimate normal variation, and why?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Write unit tests for data transforms including edge cases
- Validate incoming data for schema, ranges and missing values
- Add a fast smoke training run and model quality gates with tolerances
- Handle randomness without flaky tests
- Build regression checks for prompts and language model features
Lesson plan
- 1 What can break in ML systems Map failures in code, data and models and choose tests for each. Start
- 2 Unit tests for transforms Test feature and preprocessing functions with small, explicit cases. Start
- 3 Data validation Catch broken or unexpected data before it trains or feeds a model. Start
- 4 Smoke runs and quality gates Verify the pipeline runs and that a candidate model is not worse than the baseline. Start
- 5 Randomness and flaky tests Make checks deterministic enough to trust, and tolerant where they cannot be. Start
- 6 Testing LLM features and promotion Regression test prompt based features and promote candidates safely. Start
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About this tutor
For engineers and data scientists who want automated checks for machine learning code, data and models, not only for ordinary application code. You will write unit tests for data transforms, add data validation for schemas, ranges and missing values, run a tiny smoke training job in continuous integration, and set quality gates that compare a candidate model against a baseline with a sensible tolerance. You also tackle flaky tests caused by randomness, regression testing for prompts and language model features, keeping the pipeline fast enough that people do not bypass it, and a simple promotion path from candidate to live. Basic Python and testing knowledge is assumed.
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About the teacher
MLOps without the ceremony: tracking, versioning, monitoring and responsible deployment
9 tutors 322 lessons taught Sample
I teach the habits that keep machine learning systems trustworthy after the notebook: tracking experiments, versioning data and models, testing, monitoring, handling incidents and documenting models honestly. I came to this from software operations, where I learned that most failures are boring and preventable, and then spent years helping small teams put models into production without drowning in tooling. I...
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