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AI & MLRecommendedAIML-SYS-012

Testing Machine Learning Systems

Unit tests for data and code, behavioural tests for models, and the readiness rubric for production.

Difficulty
3 / 5
Importance
4 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can write data, model-behaviour, and infrastructure tests for an ML pipeline using an invariance or directional-expectation test.

The route to this topic

Route AIML-SYS-012
What Is a Model? Learning from Data Features and Targets Datasets and Splits Numeric and Categorical Data Linear Regression I: Prediction and Residuals Train, Validation, and Test Errors Data Validation and Exploratory Analysis Loss and Fitting: Squared Error Offline Evaluation Objective Functions and Empirical Risk Training vs Serving Data Pipelines, Feature Stores, and Training-Serving Skew Testing Machine Learning Systems