Lilly Tech Systems
AI Model Testing Fundamentals
Master the core concepts of AI model testing including metrics, validation strategies, and building comprehensive test plans for machine learning systems.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. Introduction to AI Testing
Why AI testing differs from traditional software testing
2. Test Design for ML Models
Designing effective test cases for machine learning models
3. Accuracy Precision Recall F1
Understanding and choosing the right classification metrics
4. Cross-Validation Techniques
K-fold, stratified, time series, and group cross-validation
5. Overfitting and Underfitting Detection
Identifying and diagnosing model fitting problems
6. Statistical Significance in Testing
When model improvements are real vs random chance
7. Building a Test Strategy
Comprehensive end-to-end AI testing strategy
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of ai model testing fundamentals 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.