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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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
Ensuring the reliability and robustness of machine learning models is essential to building successful ML-powered applications.
...Show More
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
An understanding of different types of testing and their importance in ML applications
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TAKEAWAY SKILLS
Unit Testing
Debugging
Data Pipeline Engineering
Data Cleaning
Course Content
32 Lessons6 Quizzes6 Challenges
1.
Introduction to Reliable ML
5 Lessons
Get familiar with enhancing machine learning system reliability through robust testing and maintenance.
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