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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

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

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. ...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

Course Content

Introduction to Reliable ML

5 Lessons

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

7 Lessons

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

4 Lessons

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

8 Lessons

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

5 Lessons

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 Lessons

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

Conclusion

Appendix

2 Lessons

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

Advanced pytest Features

Links for Additional Reading

Course Author

Arseny Kravchenko and

Bogdan Pechenkin

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AI Code Mentor

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