Machine Learning Operations - practices and tools for deploying, monitoring, and managing AI models in production, similar to DevOps but for ML systems. Like having a complete system for keeping AI models running smoothly.
MLOps teams automate model retraining, monitor performance, and quickly roll back to previous versions if a new model performs poorly.
All four clouds provide managed platforms that cover common MLOps needs: training pipelines, model registry/versioning, deployment endpoints, monitoring, and governance. Names differ, but the lifecycle stages are broadly equivalent.