mlops-python-package
Production-shaped MLOps Python package and reference implementation of the MLOps Coding Course: MLflow, scikit-learn, Pydantic, Pandera, uv, and mise.
What is mlops-python-package?
Production-shaped MLOps Python package and reference implementation of the MLOps Coding Course: MLflow, scikit-learn, Pydantic, Pandera, uv, and mise.
Overview of mlops-python-package
What it is
A production-shaped MLOps Python package and reference implementation of the MLOps Coding Course. It leverages MLflow, scikit-learn, Pydantic, Pandera, uv, and mise. The package is designed to support MLOps initiatives with flexible, robust, and productive practices.
Key features
- Uses MLflow for model tracking, registry, and evaluation - Employs Pydantic and Pandera for configuration and data schema validation - Integrates scikit-learn for model evaluation metrics - Managed with uv for package and environment management and mise for task automation - Includes a full set of tools for automation, CI/CD, CLI, code quality, configs, data handling, documentation, model management, packaging, programming, and observability - Bundles the UCI Bike Sharing dataset for reference
Use cases
- As a reference implementation for learning MLOps best practices - As a starting template for building MLOps pipelines with experiment tracking, model registry, and inference - As a toolkit for integrating with platforms like Databricks or AWS - For reproducible training, evaluation, and promotion of models using MLflow Projects
Getting started
bash # Clone the repository git clone [email protected]:fmind/mlops-python-package # or git clone https://github.com/fmind/mlops-python-package cd mlops-python-package mise trust && mise install mise run install
License and pricing
The package is licensed under MIT and is open source.
Based on the project's public README and repository metadata.
