packaging-model-for-deployment

To package a trained machine learning model, bundle the fitted preprocessing pipeline and model into a saved artifact, then generate input and output schemas, a versioned manifest, and disk-based smoke tests for FastAPI serving.

A model deployment artifact is a self-describing bundle containing the saved model, input and output schemas, and a versioned manifest. It prevents training-serving skew and eliminates guessing inputs by making the bundle reviewable and trackable for serving environments.

Yes, the model packaging approach works for non-Python runtimes like ONNX or TorchScript. It packages trained tabular, text, and deep learning models into self-describing artifacts that can be handed off to platform or SRE teams for cross-runtime serving.

Run a smoke test on a saved joblib model artifact by executing disk-based golden tests that validate the saved bundle against expected inputs and outputs. This CI gate verifies the artifact before promotion to batch jobs or streaming services.

The best way to prevent training-serving skew when shipping models to batch jobs is to package preprocessing and the model together as a single fitted pipeline. This ensures the exact same data transformations are applied during both training and inference.