Lilly Tech Systems
MLOps Testing Strategies
Testing strategies for MLOps including model training jobs, model registry, deployment testing, canary testing, and monitoring as testing.
Course Lessons
Work through these lessons sequentially or jump to the topic most relevant to you.
1. MLOps Testing Overview
Testing in the MLOps lifecycle
2. Testing Model Training Jobs
Testing training job execution
3. Testing Model Registry
Testing model registry operations
4. Deployment Testing Strategies
Testing model deployments
5. Canary and Shadow Testing
Canary and shadow deployment testing
6. Monitoring as Testing
Using monitoring as continuous testing
7. MLOps Testing Maturity Model
Measuring testing maturity
What You'll Learn
By the end of this course, you will be able to:
Core Concepts
Understand the fundamental principles and techniques of mlops testing strategies 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.