Stage 1 · Lesson 7 of 8
Tests for ML code
Most production ML failures are ordinary bugs in data loading and code, not in the maths, so test that code like any other with . Unit tests check that feature functions handle missing values, odd categories and empty inputs. Schema tests check the columns and types a function returns.
A smoke test trains on a tiny sample, or calls the model with a recorded response, and checks the output has the right shape, type and range. Keep tests fast so they run on every commit. For anything random, fix the seed or test a range rather than an exact value.
Common trapasserting that accuracy equals an exact number, which fails on every harmless change and teaches the team to ignore red builds.
A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock
Resources for this stage
Start with· follow one of these
- CoursePaidGrokking Python Fundamentals
Design Gurus · Beginner · $50
If your Python stops at notebooks: modules, functions, classes and error handling done properly
- CourseFreeMade With ML
Goku Mohandas · Advanced
Free: the scripting, CLI, testing and documentation lessons turn notebook code into a real project