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

A software that allows you to protect your system from hacker attacks, such as antiviruses

A machine learning system to combat bots, spam, and unusual user behavior (anti-fraud system)

A system that saves your code from potential bugs in the future

User authorization techniques and instruments used when accessing our service

Prevent code that is covered with tests from changes (freeze the codebase)

Check how different components work together

**(Select all that apply.)** What purposes do the tests serve?

(Select all that apply.) What purposes do the tests serve?

**(Select all that apply.)** Which properties of tests increase when moving up in the Martin Fowler pyramid?

(Select all that apply.) Which properties of tests increase when moving up in the Martin Fowler pyramid?

**(Select all that apply.)** What are  examples of ML-specific tests?

(Select all that apply.) What are examples of ML-specific tests?

General software testing is not relevant to ML projects.

General software testing should make up the majority of testing for an ML project, with ML-specific testing making up the remainder.

ML-specific testing should be the primary focus of testing for ML projects.

What is the relationship between general software and ML-specific testing?

What is the relationship between general software and ML-specific testing?

Take a short quiz to test your knowledge about defensive programming and testing.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Quiz: Introduction to Reliable ML