mlops-wrapper 0.1.1

a unified mlflow wrapper for standardized ML model logging and registration.

mlops-wrapper

custom python wrapper around mlflow

a unified mlflow wrapper that standardizes experiment tracking, artifact logging, and model registration across teams and projects. by providing decorators and base classes, this wrapper reduces boilerplate code and enforces best practices for mlflow usage.

features

• consistent logging: standardized interface for logging parameters, metrics, and artifacts.
• model registry integration: automatically register models in mlflow’s model registry.
• decorator & base class: use a python decorator (@mlflow_experiment) or extend baseexperiment to minimize boilerplate code.
• error handling: automatically logs exceptions and ensures mlflow runs are closed gracefully.
• flexible storage: works with local or remote mlflow servers; supports s3 or local file system artifact stores.

usage

yet to be written