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 of RODE

Before practicing with tests, let's imagine we build a simple ML-based web service, RODE (Rotated Object Detector). It takes some image as input, detects an object (its coordinates and angle because the object may be rotated), crops the object, rotates it back, and saves the result.

This scenario is very realistic. For example, you can imagine using something like this as part of the application that watches your home entrance and sends photos of people near your door. 

## Interacting with RODE

Our web service has a single entry point: a `POST` request that sends an image and receives detection results.



# Overview of RODE

Before practicing with tests, let's imagine we build a simple ML-based web service, RODE (Rotated Object Detector). It takes some image as input, detects an object (its coordinates and angle because the object may be rotated), crops the object, rotates it back, and saves the result.

This scenario is very realistic. For example, you can imagine using something like this as part of the application that watches your home entrance and sends photos of people near your door. 

# Interacting with RODE

Our web service has a single entry point: a `POST` request that sends an image and receives detection results.



Learn how to test RODE, a simple ML-based web service for detecting and rotating objects in images.

Introduction to Reliable ML

Software Testing

Best and Worst Practices

ML-Specific Tests

ML Software Reliability outside of Tests

Wrapping Up

Appendix

Example: Microservice Lacking Tests

Overview of RODE

Interacting with RODE