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

To start our journey, we'll only need a single new tool—`pytest`. It is not part of Python's standard library; however, it's the most helpful tool for testing Python code.

pytest is a testing framework for Python that makes it easy to write and run tests. It allows you to use plain Python code and has automatic test discovery and execution in your project. It also provides useful features such as the built-in assert statement and fixtures for managing test data. It's a popular choice for testing in the Python community because of its ease of use and flexibility.


> **Note:** The Python standard library contains another testing library—`unittest`. However, it's less popular because of the less intuitive API influenced by the Java testing library.

Here's an example of a `pytest` test. It creates a 100x100 image containing a 10x10 square. The function `crop_image`, which can be found in the `utils.py` file, crops an image based on passed coordinates `(x, y)` and cropped image resolution `(w, h)`.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Introduction to pytest

Overview