Apply Test-Driven ML Code

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

• Apply test-driven development to ensure reliable ML codebases
• Apply modular design principles to ML components like data loaders and training loops
• Ensure production-quality ML code with comprehensive testing and CI/CD

• Basic familiarity with machine learning concepts and terminology
• Willingness to practice through applied exercises or case-based work