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

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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

When the code of our machine learning service is written and put into production, it should run as smoothly as a living organism. We don't always have the option to intervene and fix something on the go. There aren't many things to do to make this code reliable, but we'll discuss most of them in this section.

### Fail fast vs. fail safe

Depending on the domain and application, we should either process exceptions/bugs silently (log or trigger an alert and continue running) or completely stop runtime. This decision is also called the robustness vs. correctness issue.



# Overview

When the code of our machine learning service is written and put into production, it should run as smoothly as a living organism. We don't always have the option to intervene and fix something on the go. There aren't many things to do to make this code reliable, but we'll discuss most of them in this section.

## Fail fast vs. fail safe

Depending on the domain and application, we should either process exceptions/bugs silently (log or trigger an alert and continue running) or completely stop runtime. This decision is also called the robustness vs. correctness issue.



Learn about assertions and when to run them.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Runtime Checks

Overview

Fail fast vs. fail safe