Principle:Mlflow Mlflow Model Logging
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Model_Management |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Persisting a trained model in a standardised format alongside its metadata, dependencies, and input/output signature ensures reproducible deployment and governance.
Description
Model logging is the act of serialising a fitted model object and storing it as a versioned artifact within an experiment tracking system. Beyond the raw model weights, the logging step captures the runtime environment (Python version, package dependencies), a description of the expected input and output schemas (the model signature), optional example inputs, and arbitrary metadata. Together these artefacts form a self-contained package that any downstream consumer -- whether a batch scoring pipeline, a REST endpoint, or a notebook analyst -- can load without ambiguity about how the model was produced or what it expects.
A well-designed logging mechanism abstracts over framework-specific serialisation formats. It wraps a scikit-learn pickle, a PyTorch state dictionary, or an ONNX graph in a uniform envelope so that consumers need only understand a single loading interface. This abstraction is the key enabler for framework-agnostic deployment.
The logging step also serves as the bridge between experiment tracking and model registry. When a model is logged with a registered model name, the platform can automatically create a new version in the registry, linking the run-level artifact to a governed lifecycle entry without requiring a separate registration call.
Usage
Use model logging immediately after training and evaluation, while the model object is still in memory and the run context is active. Provide a model signature and an input example whenever possible, as these enable downstream validation, documentation generation, and serving infrastructure to operate correctly.
Theoretical Basis
Model logging operationalises the principle of reproducibility in machine learning. A logged model is not merely a set of weights; it is a complete specification that includes the computational environment, the data contract (signature), and the code required to reconstitute the prediction function. This aligns with the concept of a model card or model manifest -- a portable description that travels with the artifact and allows any consumer to understand and trust the model without access to the original training code.
The standardised format also enables automated compatibility checks at deployment time, ensuring that the serving environment satisfies the dependency requirements recorded at logging time and that input data conforms to the declared schema.