fmind/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 it solves

It provides a standardized Python codebase and a set of best practices to streamline MLOps initiatives. It solves the problem of fragmented or inconsistent project structures when building MLOps platforms, such as model registries, experiment tracking systems, or real-time inference services.

How it works

The project acts as a reference implementation and toolkit that integrates a wide array of professional software engineering tools. It uses a configuration-driven approach where jobs (like training, tuning, or inference) are defined in YAML files and executed via a CLI. The system leverages Pydantic for configuration validation and uses a modular "job" architecture that can be used as a standalone package or integrated as a library into other AI/ML projects.

Who it’s for

ML engineers and software developers who want to implement a robust, production-ready MLOps pipeline using industry-standard tools for testing, linting, CI/CD, and model management.

Highlights

  • Comprehensive Toolchain: Integrates tools like uv for package management, Ruff for linting/formatting, Pytest for testing, and MLflow for tracking and registry.
  • Configuration-Driven: Uses YAML and OmegaConf to define job behaviors, allowing for flexible execution without changing code.
  • Automated Workflows: Includes pre-defined GitHub Actions for CI/CD and a mise task runner to synchronize local development and CI environments.
  • Strong Typing: Emphasizes static typing and data schema validation using ty and Pandera for dataframes.

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