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

As we develop machine learning projects, ensuring that our code is reliable and free of errors is important. One way to do this is through testing automation, which involves using tools and techniques to automate the testing process. 

This lesson will discuss code coverage and continuous integration, two important concepts in testing automation. **Code coverage** helps us understand how much of our code is being tested, while **continuous integration** allows us to automatically build, test, and deploy our code with each change. Using these techniques, we can ensure that our code works as intended and catch any issues early on in the development process.

# Test coverage

**Test coverage** refers to the amount of code tested during the testing process. It helps us understand how much of our code is being tested and identify any areas that may not be thoroughly tested. By having a high test coverage, we can have confidence in the reliability of our code and ensure that it performs as expected.

> **Note:** Test coverage is an important indicator regarding simplicity, quality, and effectiveness in software testing.

Test coverage and code coverage are often used interchangeably, but some people distinguish between the two terms. Test coverage is a metric reflecting the amount of codebase used when running a complete set of tests. More tests can help us discover new bugs in our code more quickly, especially if the tests are well-designed and cover many scenarios.

## What does test coverage do?

* **Determine uncovered areas:** By calculating test coverage, we can easily identify the areas in the software that the current set of test cases has not tested.

* **Improve test cases:** This process helps to create more test cases that are needed to increase the software's coverage and ensure it works as intended.

* **Measure quality:** Test coverage provides a quantitative measure of the testing efforts and can indirectly indicate the software's quality.

* **Eliminate inefficient tests:** It also helps to eliminate test cases that do not contribute to increasing coverage, making the testing process more efficient and focused.

Usually, we use **line-wise coverage**: a simple ratio between the number of lines covered by tests to the total number of lines in the project.



Learn how to automate your project's testing and evaluate how well it's tested.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Automation of Testing

Overview

Test coverage