model-deployment

Production deployment requires packaging your trained model with versioning metadata, serializing it to a durable format like pickle or ONNX, wrapping it in an API endpoint, and adding health checks for monitoring. This Skill guides the complete workflow from notebook to containerized service.

Serialize models using pickle, joblib, or ONNX format alongside version metadata to track model iterations. Versioning enables reproducibility, rollback capability, and audit trails across CI/CD pipelines and production deployments.

Yes. ONNX export enables your model to run across different platforms and frameworks independent of its original training environment, making it ideal for deploying to diverse production systems.

FastAPI endpoints expose your model as a RESTful service with input/output schema validation. Define request and response schemas, load your serialized model, and create endpoints that handle predictions and return structured results.

Basic telemetry metadata and health check endpoints verify your model service is running and responsive. These catch failures early and provide observability into model availability and performance in production pipelines.

Yes. Dockerizing your FastAPI service alongside versioned model artifacts ensures consistent deployments across environments. Container images package the model, API code, and dependencies as a single reproducible unit.