MyAwesomeDocker.tar.gz

MyAwesomeDocker

TorchJob

spa_run

spa_run_python

mypy_example

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

Congratulations on completing the course. By now, you should understand how to make your machine learning software more reliable through various testing approaches and best practices.

Remember, the importance of testing goes beyond just finding bugs. It ensures that your software is reliable, maintainable, and scalable, allowing you to deploy it in production confidently. And when it comes to machine learning, testing is even more critical because it helps you ensure your models make accurate predictions and your pipeline is working as intended.

Learn what the next steps are after completing the course.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Conclusion