ds-mlops-deployment

Deploy a trained ML model to production by packaging the serialized artifact into a serving pattern such as a REST API, batch job, or scheduled pipeline. This enables real-time inference and continuous monitoring for the deployed service.

Model drift detection monitors production data for statistical changes that degrade prediction accuracy over time. It triggers automated retraining workflows to sustain model performance and keep the deployed service healthy without manual intervention.

MLflow model registries store serialized models and experiment tracking metadata for reproducible deployment. Registering models in MLflow creates a governed lifecycle pipeline that transitions validated artifacts directly into production serving environments.

CI/CD workflows for ML model serving automate the transition from serialized training artifacts to production services. Integrating continuous integration pipelines with drift detection triggers enables automated retraining and governance across the entire model lifecycle.

Containerize an ML model by packaging the serialized artifact with its dependencies into a portable unit for batch and streaming serving. This containerization approach standardizes deployment patterns across scheduled pipelines and embedded service components.

Production model accuracy degrades over time due to data drift when input distributions change without monitoring. Implementing drift detection with automated retraining triggers sustains prediction accuracy by identifying statistical shifts and initiating model updates.