Lilly Tech Systems
Test-Driven ML Development
Apply test-driven development principles to machine learning with data contract testing, model behavior tests, and continuous testing workflows.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. TDD Principles for ML
Adapting TDD for machine learning
2. Writing Tests Before Models
Writing tests before building models
3. Data Contract Testing
Testing data contracts between teams
4. Model Behavior Tests
Testing expected model behaviors
5. Continuous Testing Workflows
Integrating tests into ML workflows
6. Refactoring ML Code Safely
Refactoring with test safety nets
7. TDD Case Studies
Real-world TDD success stories
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of test-driven ml development for production AI systems.
Practical Skills
Build hands-on skills with real code examples, frameworks, and tools used by industry professionals.
Best Practices
Apply industry best practices and avoid common pitfalls when implementing testing in your ML projects.
Production Ready
Ship reliable, well-tested AI systems with confidence using automated testing pipelines.