# microsoft/MLOpsPython

MLOps using Azure ML Services and Azure DevOps

Repository: https://github.com/microsoft/MLOpsPython
Canonical: https://ross.abutalabs.com/products/mlopspython
Language: Python
License: MIT
License Family: permissive
Topics: azure-machine-learning, mlops, azureml
Last push: 2023-08-05T09:57:11+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2773, "days_push": 1124, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1318, forks 1159 (observed 2026-08-28T04:04:21.088145+00:00)

## What it is
A Microsoft sample/template repository demonstrating how to build CI/CD pipelines for machine learning projects using Azure Machine Learning and Azure DevOps. It includes pipeline definitions for data sanity tests, model training, evaluation, versioning, and staged deployment as a realtime web service.

## Use cases
- set up mlops pipelines for a python ml project on azure
- automate model training and deployment with azure devops
- learn how to operationalize machine learning workflows
- template for ci/cd for scikit-learn models
- build a model retraining pipeline in azure ml
- deploy ml models as realtime web services with staged qa/prod releases

## When to choose
- you use Azure Machine Learning and Azure DevOps and want a reference implementation
- you need a starting template for end-to-end ML lifecycle automation on Azure
- you want to learn MLOps practices like model evaluation, versioning, and staged deployment

## When to avoid
- you don't use Azure (the pipelines and services are Azure-specific)
- you need a production-grade maintained tool rather than a sample/template
- you use other ML platforms like SageMaker, Vertex AI, or Kubeflow

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: ci-cd, machine-learning, deployment, testing, workflow-automation
- domain: machine-learning, cloud-computing
- platform: python, cloud
- tags: mlops, azure-machine-learning, azure-devops, reference-architecture, sample-template, model-deployment, retraining-pipeline, devops, azure, docker

## Member repositories
- microsoft/MLOpsPython (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:21.088145+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 2026-08-30T04:47:58.474509+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/MLOpsPython (fetched 2026-08-28T04:04:21.088145+00:00, sha 4a61f8c975d7)
- Data as of 2026-08-30T08:39:29.467469+00:00.