Reliable Machine Learning - AI-Powered Learning for Developers

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.

Intermediate

32 Lessons

Certificate of Completion

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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

29 Playgrounds

6 Challenges

6 Quizzes

This course includes

29 Playgrounds

6 Challenges

6 Quizzes

Course Overview

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...Show More

TAKEAWAY SKILLS

Unit Testing

Debugging

Data Pipeline Engineering

Data Cleaning

What You'll Learn

An understanding of different types of testing and their importance in ML applications

Familiarity with using Pytest to enhance the robustness of machine learning systems

An in-depth understanding of the best (and worst) practices of testing

Hands-on experience monitoring machine learning applications for issues

What You'll Learn

An understanding of different types of testing and their importance in ML applications

Course Content

Introduction to Reliable ML

Get familiar with enhancing machine learning system reliability through robust testing and maintenance.

Who Should Take This Course?

Why Do We Need Tests?

Various Software Testing Paradigms Example: Microservice Lacking Tests Quiz: Introduction to Reliable ML

Software Testing

Solve challenges with unit testing, pytest, integration testing, and advanced software testing techniques.

Unit Testing Introduction to pytest Fixtures in pytest Integration Testing Mocks and Patches Mutation and Property-Based Tests Quiz: Software Testing

Best and Worst Practices

Examine best practices and pitfalls in test-driven development, negative versus flaky tests, and test automation.

Test-Driven Development Flaky and Negative Tests Automation of Testing Quiz: Best and Worst Practices

ML-Specific Tests

Apply your skills to implement robust ML-specific tests ensuring reliability and consistency.

Why Machine Learning is Specific Behavioral Testing Comparing the Environments Tests for Exact Values Smoke Tests for the ML Pipeline Fixtures and Mocks in Machine Learning Tests Samples as Tests Quiz: ML-Specific Tests

ML Software Reliability outside of Tests

Improve ML service reliability using robust runtime checks, type hinting, logging, and monitoring.

Runtime Checks Type Hinting Logging and Debugging ML Monitoring Guide Quiz: ML Software Reliability Outside of Tests

Wrapping Up

1 Lesson

Focus on implementing testing to enhance machine learning software's reliability and scalability.

Appendix

2 Lessons

Master advanced pytest features and access key resources for enhancing machine learning reliability.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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