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

They treat the whole project as a single unit.

They sufficiently cover all the logic of the code base.

**(Select all that apply.)** What are the benefits of unit tests?

(Select all that apply.) What are the benefits of unit tests?

A code mimicking all the inputs and outputs of the testable one.

Fixed version of the code with all its inputs and outputs to compare its behavior with later versions in the future.

Fixed environment items (e.g., function inputs and outputs) used in tests.

They test a larger part of the codebase at once.

They check the higher-level logic of the code.

They check how the individual components of the code work together.

**(Select all that apply.)** How are integration tests different from unit tests?

(Select all that apply.) How are integration tests different from unit tests?

To develop integration tests without launching additional services required for the code we try to test.

To count the number of calls of a particular function.

To provide the unit tests with a simulated environment.

Take a short quiz to test how well you understand software testing.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Quiz: Software Testing