Lilly Tech Systems
A/B Testing for AI Systems
Design and analyze experiments for AI systems including sample size calculation, statistical analysis, multi-armed bandits, and feature flags for ML models.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. A/B Testing Fundamentals
Core A/B testing concepts for AI
2. Designing AI Experiments
Designing rigorous AI experiments
3. Sample Size Calculation
Calculating required sample sizes
4. Statistical Analysis Methods
Analyzing experiment results
5. Multi-Armed Bandits
Adaptive experimentation strategies
6. Feature Flags for ML Models
Using feature flags for model rollout
7. Analyzing Experiment Results
Interpreting and acting on results
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of a/b testing for ai systems 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.