fmind/mlops-python-package

A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.

What it solves

This project provides a standardized Python codebase and a set of best practices for MLOps, helping developers avoid starting from scratch when building model registries, experiment tracking systems, or real-time inference platforms. It streamlines the transition from experimental code to production-ready software by integrating a comprehensive suite of professional software engineering tools.

How it works

The package is structured as a modular framework where program behavior is controlled via YAML configuration files. Users can define specific jobs (such as training, tuning, or inference) in src/[package]/jobs/*.py and execute them using a CLI that merges configuration files. It leverages a modern Python toolchain including uv for dependency management, Pydantic for configuration validation, and MLflow for model tracking, registry, and observability.

Who it’s for

It is designed for ML engineers and data scientists who want to implement a robust, production-grade MLOps toolkit or platform while following industry best practices in software engineering and AI/ML lifecycle management.

Highlights

  • Integrated MLOps Stack: Built-in support for MLflow (tracking, registry, projects) and SHAP for explainability.
  • Comprehensive Automation: Uses mise to provide a unified task vocabulary for installation, testing, linting, and building.
  • Production-Ready Tooling: Incorporates Ruff for linting/formatting, Pytest for testing, and Docker for runtime packaging.
  • Flexible Configuration: Employs OmegaConf and Pydantic Discriminated Unions to automatically select and run different job types based on config files.

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