ds-mlops-deployment

What problem does it solve?

Deploys trained ML models to production and keeps them healthy over time. This section describes bridging the gap between training and production by packaging, serving, monitoring, and governing models in real systems.

Core Features & Use Cases

• Experiment tracking with MLflow and model registries for reproducibility.
• Production serving as REST API, batch job, scheduled pipeline, or embedded service.
• Monitoring, drift detection, and retraining triggers to sustain model accuracy over time.
• CI/CD integration for ML workflows and governance across the lifecycle.

Quick Start

Train and deploy a trained, serialized model to production with monitoring enabled.