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Apply Test-Driven ML Code

This course is part of multiple programs.

Instructor: Hurix Digital

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 hour to complete

Flexible schedule

Learn at your own pace

What you'll learn

Test-driven development creates a safety net that enables confident refactoring and continuous improvement of ML codebases for reliable systems.
Modular design principles applied to ML components (data loaders, training loops) dramatically improve code reusability and team collaboration.
Production-quality ML code requires the same software engineering rigor as traditional development, including comprehensive testing and CI/CD.
Investing in code quality upfront prevents technical debt that can derail ML projects during scaling and deployment phases of development.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

Did you know that over 70% of machine learning failures in production stem from fragile, untested code rather than faulty models? Test-driven development is the key to writing ML pipelines that are reliable, reusable, and production-ready.

This Short Course was created to help professionals in this field develop robust and maintainable ML code that meets production standards and enables effective team collaboration. By completing this course, you will be able to write modular ML components, build test-driven data loaders and training loops, and ensure your codebase is resilient to change and easy for teams to maintain—skills that strengthen both software quality and ML workflow reliability. By the end of this 3-hour long course, you will be able to: Apply modular and test-driven development principles to code data loaders and training loops. This course is unique because it merges software engineering best practices with practical ML development, giving you hands-on experience in creating clean, testable, and scalable ML code that supports long-term production success. To be successful in this project, you should have: Python programming experience Basic ML concepts Familiarity with TensorFlow Unit testing fundamentals

Module details

Learners will establish foundational understanding of test-driven development principles and modular architecture patterns specifically applied to machine learning code components.

What's included

3 videos1 reading1 assignment

Learners will implement production-quality DataLoader classes and training loops using TDD principles, creating comprehensive test suites and establishing CI/CD integration workflows.