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.

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

## Overview

The `pytest` library is powerful, and  we won't be able to describe all its features here. However, we can still dive into a few to show them as some bait. 

# Overview

The `pytest` library is powerful, and  we won't be able to describe all its features here. However, we can still dive into a few to show them as some bait. 

Learn two more tricks that you can use in pytest: tests that are expected to fail and log capturing.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Advanced pytest Features

Overview