What is Model Packaging and Why Does It Matter?

Model Packaging: The process of bundling a trained model with its dependencies, preprocessing logic, and configuration into a deployable artifact. A well-packaged model contains everything needed to run inference without relying on external training infrastructure or implicit environment assumptions.

The Reproducibility Problem

A model file alone is insufficient for production. It requires: the correct framework version (TensorFlow 2.12, not 2.10), specific library versions (numpy 1.23 with particular BLAS bindings), preprocessing code that matches training (same tokenizer, same normalization constants), and hardware compatibility (GPU drivers, CUDA versions). Without explicit packaging, deployment becomes "it works on my machine" scaled to production—models that ran perfectly in development fail mysteriously in serving environments.

Packaging Layers

Model serialization: Save model weights and architecture in a portable format (SavedModel, ONNX, TorchScript). This captures the computational graph independent of training code. Dependency specification: Pin exact versions of all libraries (requirements.txt, conda environment). Include system-level dependencies (CUDA, cuDNN versions). Runtime container: Package everything in a Docker image with known base OS, framework installation, and inference entry point. The container becomes the deployable unit.

What Gets Packaged

Model weights (the trained parameters), model architecture (layer definitions, graph structure), preprocessing artifacts (tokenizers, vocabulary files, normalization statistics), configuration (input/output shapes, batch size limits), and inference code (the serving function that accepts requests and returns predictions). Missing any component causes production failures: wrong preprocessing produces garbage inputs; missing configuration causes shape mismatches; outdated inference code returns wrong output formats.

Key Insight: Good packaging makes deployment deterministic. Given the same model package and the same input, the output is identical regardless of which server runs inference or when it runs.