# fmind/mlops-python-package

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

Repository: https://github.com/fmind/mlops-python-package
Canonical: https://ross.abutalabs.com/products/mlops-python-package
Homepage: https://fmind.github.io/mlops-python-package/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: mlops, python, automation, data-pipelines, data-science, machine-learning, mlflow, pandera, pydantic, data-engineering, machine-learning-operations, python-template
Last push: 2026-08-24T01:28:35+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 85, longevity 83
- inputs: {"age_days": 1167, "days_push": 10, "days_rel": 23, "gap_med": 56.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1415, forks 200 (observed 2026-08-28T04:04:39.815062+00:00)

## What it is
A Python package template that provides a production-grade code base with MLOps best practices for building and deploying machine learning projects. It integrates tooling for CI/CD, testing, linting, experiment tracking, and data pipeline validation to standardize MLOps initiatives.

## Use cases
- kickstart an mlops python project
- template for machine learning package with best practices
- set up ci/cd for ml pipelines
- standardize data science codebase structure
- learn mlops project layout and tooling
- bootstrap mlflow experiment tracking project
- production-ready python package for data pipelines

## When to choose
- you are starting a new Python MLOps or data pipeline project and want battle-tested conventions
- you want integrated CI/CD, testing, linting, and experiment tracking out of the box
- you want a reference implementation of MLOps best practices to learn from

## When to avoid
- you need a full MLOps platform rather than a code template
- your team already has established project conventions and tooling
- you need a framework with runtime orchestration features rather than a starting codebase

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, etl, developer-tools, ci-cd, testing, logging, configuration-management, deployment
- domain: machine-learning, data-science, developer-tools
- platform: python, cross-platform
- tags: mlops, boilerplate, project-template, cookiecutter, mlflow, pandera, pydantic, data-pipelines, best-practices, template, data-engineering, automation, docker, github-actions

## Member repositories
- fmind/mlops-python-package (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:39.815062+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:20:16.839192+00:00, confidence not recorded.
  - readme: https://github.com/fmind/mlops-python-package (fetched 2026-08-28T04:04:39.815062+00:00, sha 9cdb8856dde9)
  - homepage: https://fmind.github.io/mlops-python-package/ (fetched 2026-08-29T11:50:58.086240+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.