Automated testing for ML code in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is automated testing in ML code?

Automated testing in ML code means using tools to check if machine learning programs work correctly without doing it by hand. It helps find mistakes early and keeps the ML system reliable.

beginner

Name one type of test used specifically for ML code.

Data validation tests check if the input data is correct and clean before training the ML model. This prevents errors caused by bad data.

intermediate

Why is testing ML models different from testing regular software?

ML models learn from data and can change behavior with new data. So, tests must check data quality, model accuracy, and performance, not just code correctness.

beginner

What is a unit test in the context of ML code?

A unit test checks a small part of the ML code, like a function that cleans data or calculates a metric, to make sure it works as expected.

intermediate

How can continuous integration (CI) help with automated testing for ML?

CI runs tests automatically every time code changes. For ML, it can run data checks, retrain models, and test results to catch problems early.

Which test checks if the input data to an ML model is clean and correct?

A Load test

B Unit test

C Integration test

D Data validation test

Data validation tests ensure the input data quality before training or prediction.

What is the main goal of automated testing in ML code?

A To manually check every line of code

B To automatically verify ML code and model correctness

C To replace the ML model with a simpler one

D To speed up data collection

Automated testing helps verify ML code and models without manual effort.

Which of these is NOT typically tested in ML automated testing?

A Model accuracy

B Data quality

C User interface colors

D Code functions

User interface colors are unrelated to ML code testing.

What does a unit test in ML code usually focus on?

A Testing small parts like functions

B Testing user feedback

C Testing hardware performance

D Testing the entire ML pipeline

Unit tests check small code parts to ensure they work correctly.

How does continuous integration (CI) improve ML testing?

A By automatically running tests on every code change

B By removing the need for tests

C By running tests only once a year

D By manually reviewing code

CI automates running tests whenever code changes, catching issues early.

Explain why automated testing is important for machine learning code.

Think about how testing helps keep ML systems reliable and efficient.

Describe different types of tests used in automated testing for ML code.

Consider tests that check code, data, and model behavior.