A crash course on making ML software more reliable, including best practices on testing and other aspects of defensive programming.
Intermediate
32 Lessons
8h
Certificate of Completion
A crash course on making ML software more reliable, including best practices on testing and other aspects of defensive programming.
AI-POWERED
AI-POWERED
This course includes
This course includes
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
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Course Content
Introduction to Reliable ML
Software Testing
Best and Worst Practices
ML-Specific Tests
ML Software Reliability outside of Tests
Wrapping Up
1 Lesson
Course Author
Trusted by 1.4 million developers working at companies
Anthony Walker
@_webarchitect_
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
Souvik Kundu
Front-end Developer
Eric Downs
Musician/Entrepeneur
Anthony Walker
@_webarchitect_
Evan Dunbar
ML Engineer
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