Explore how to ensure reliability in ML models. Gain insights into software testing, ML-specific techniques, runtime checks, and monitoring tools to build robust ML systems effectively.

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Ensuring the reliability and robustness of machine learning models is essential to building successful ML-powered applications.

This course begins with a thorough introduction to software testing essentials, particularly use cases within the machine learning context. You’ll learn about topics related to software testing, including unit and integration testing and more advanced testing techniques. Next, you’ll learn the best practices in software testing and dive into ML-specific testing techniques, such as behavioral and smoke tests. Lastly, you’ll cover the aspects of ML software reliability outside of testing, including runtime checks and type hinting.

By the end of this course, you'll be equipped with the knowledge and skills to ensure the reliability and robustness of your machine learning systems. You’ll be able to apply software engineering principles to your ML processes, create and execute efficient testing approaches, and utilize monitoring tools to identify and resolve problems in your ML systems.

Reliable Machine Learning

There are several software testing techniques, some of which we discussed earlier. Let's now discuss mutation and property-based testing techniques.

# Mutation tests

How do you know if your tests validate your logic, or are the problems of the main code and test code just luckily matched? 

**Mutation testing** is a technique used to test the effectiveness of a set of test cases in finding defects in a piece of software. It works by introducing small, intentional changes (called mutations) to the software being tested and then running the test cases to see if they can detect the change. The goal of mutation testing is to ensure that the test cases are robust and can detect any changes that might indicate a defect in the software.

To perform mutation testing, a tool is used to make the mutations in the code and then run the test cases. The tool then records which mutations were detected by the test cases and which were not. This allows developers to see which parts of the code were not adequately tested and add additional test cases as needed.



Learn what mutation and property-based tests are.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Mutation and Property-Based Tests

Mutation tests