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4.5

Intermediate

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.

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Overview

Content

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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. ...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

TAKEAWAY SKILLS

Unit Testing

Debugging

Data Pipeline Engineering

Data Cleaning

Content

32 Lessons6 Quizzes6 Challenges

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Arseny Kravchenko and

Bogdan Pechenkin

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Personalized Interview Prep

Skip the LeetCode grind with a custom roadmap that adapts to your goals. Hands-on practice for Coding Interviews, System Design, and more.

Mock Interviews

Test your skills in a simulated interview setting. Receive personalized feedback based on your performance. Available for Coding Interviews, System Design, and more.

AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

Explain with AI

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AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

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