This Python package provides a robust and flexible foundation for MLOps initiatives, targeting engineers and researchers who need to build and deploy machine learning systems. It streamlines common MLOps tasks like experiment tracking, model registry, and inference by integrating best practices and a curated set of developer tools.

How It Works

The package employs a configuration-driven approach using YAML files and Pydantic for validation, allowing users to define and execute various ML jobs (training, tuning, inference) without modifying core code. It leverages Python's object-oriented features and design patterns like DAGs for pipeline orchestration, promoting modularity and maintainability. Key integrations include MLflow for tracking and registry, Ruff for fast linting and formatting, and uv for efficient package management.

Quick Start & Requirements

• Install with uv sync after cloning the repository.
• Requires Python >= 3.13 and uv >= 0.5.5.
• Official documentation: https://docs.astral.sh/uv/

Highlighted Details

• Comprehensive toolchain: Integrates Bandit, Commitizen, Mypy, Pandera, Pytest, Ruff, and more for code quality, security, and testing.
• Configuration-as-code: Uses YAML and OmegaConf for flexible job definition and execution.
• MLflow integration: Supports experiment tracking, model registry, and lineage.
• Package management: Utilizes uv for efficient dependency resolution and package building.

Maintenance & Community

The project is maintained by fmind. Further community or roadmap information is not explicitly detailed in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The MLflow SHAP module is noted as not mature enough, and SHAP itself can be slow on large datasets. Plyer is not recommended for large-scale projects. The package does not explicitly mention support for Windows or macOS, focusing on Python and Docker environments.