Automate, Validate, and Promote ML Models Safely
Completed by Jun Hansen
June 18, 2026
2 hours (approximately)
Jun Hansen's account is verified. Coursera certifies their successful completion of Automate, Validate, and Promote ML Models Safely
What you will learn
Reliable MLOps depends on systematic diagnosis: performance issues are solved by log analysis and pipeline investigation, not guesswork.
Governance must be automated into deployment—responsible AI needs CI/CD checks for fairness, explainability, and safe rollbacks, not manual reviews.
Adaptive systems need intelligent automation—production models should monitor drift and trigger retraining automatically to stay accurate.
Operational excellence requires end-to-end visibility, strong monitoring, versioning and audit trails enable fast debugging and long-term reliability
Skills you will gain
- Category: Model Evaluation
- Category: Data Ethics
- Category: Cloud Platforms
- Category: Performance Tuning
- Category: Responsible AI
- Category: Continuous Delivery
- Category: Continuous Deployment
- Category: MLOps (Machine Learning Operations)
- Category: Model Deployment
- Category: CI/CD
- Category: Continuous Integration
- Category: Automation

