Lilly Tech Systems
Automated ML Testing Pipelines
Build CI/CD pipelines for ML testing with GitHub Actions, automated data validation, model quality gates, and end-to-end pipeline monitoring.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. ML Testing Pipeline Architecture
Designing ML testing pipelines
2. GitHub Actions for ML Tests
CI/CD with GitHub Actions for ML
3. Automated Data Validation Steps
Automating data validation in pipelines
4. Model Quality Gates
Implementing quality checkpoints
5. Artifact Versioning and Testing
Versioning and testing ML artifacts
6. Pipeline Monitoring
Monitoring ML pipelines
7. End-to-End Pipeline Example
Complete pipeline walkthrough
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of automated ml testing pipelines 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.