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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.

Intermediate

32 Lessons

8h

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.
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Explanations

AI-POWERED

Explanations

This course includes

29 Playgrounds
6 Challenges
6 Quizzes
Course Overview
What You'll Learn
Course Content
Recommendations

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. This course begins with a thorough introduction to software testing essentials, particularly use cases within the machine learn...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
An understanding of different types of testing and their importance in ML applications

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Course Content

1.

Introduction to Reliable ML

5 Lessons

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

2.

Software Testing

7 Lessons

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

3.

Best and Worst Practices

4 Lessons

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

5.

ML Software Reliability outside of Tests

5 Lessons

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

6.

Wrapping Up

1 Lessons

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

7.

Appendix

2 Lessons

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

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