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.
These managed ML platforms provide core MLOps capabilities such as model training, model registry/versioning, deployment endpoints, monitoring, and pipeline automation. They differ in integrations and tooling, but each supports end-to-end model lifecycle management.