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

## Overview of integration tests

**Integration tests** check how several system components work together. For example, integration testing includes launching complex functions that require the operation of multiple simple ones, running pipelines (sequences of transformations), or testing APIs.

Compared to unit tests, integration tests belong to a higher level of testing. In practice, they tend to be slower; they affect several components and require more maintenance work.

Speaking of required tools, one may start with the same base `pytest`, although integration tests may leverage using additional tools like `unittest` for mocks, patches, etc. Let’s look at an example of an integration test below:



# Overview of integration tests

**Integration tests** check how several system components work together. For example, integration testing includes launching complex functions that require the operation of multiple simple ones, running pipelines (sequences of transformations), or testing APIs.

Compared to unit tests, integration tests belong to a higher level of testing. In practice, they tend to be slower; they affect several components and require more maintenance work.

Speaking of required tools, one may start with the same base `pytest`, although integration tests may leverage using additional tools like `unittest` for mocks, patches, etc. Let’s look at an example of an integration test below:



Learn about integration testing and how it's different from unit testing.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Integration Testing

Overview of integration tests