Lilly Tech Systems
Data Validation & Testing
Learn data quality testing with Great Expectations, schema validation, statistical data tests, and automated data profiling for production ML systems.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. Data Quality Fundamentals
Core principles of data quality for ML
2. Schema Validation
Validating data schemas and types
3. Statistical Data Tests
Statistical methods for data quality
4. Great Expectations Framework
Using Great Expectations for data testing
5. Data Drift Detection
Detecting distribution changes in data
6. Automated Data Profiling
Profiling data automatically
7. Data Testing in Production
Running data tests in production systems
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of data validation & testing 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.