Reliable Machine Learning
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.
5.0
32 Lessons
8h
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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.
Ensuring the reliability and robustness of machine learning models is essential to building successful ML-powered applications.
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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
Learning Roadmap
Certificate of Completion
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