# kelvins/awesome-mlops

:sunglasses: A curated list of awesome MLOps tools

Repository: https://github.com/kelvins/awesome-mlops
Canonical: https://ross.abutalabs.com/products/kelvins-awesome-mlops
Language: Python
License Family: other
Topics: awesome, machine-learning, machine-learning-engineering, mle, mlops, data-science, ml, ai
Last push: 2026-08-17T00:21:28+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 2291, "days_push": 17, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5254, forks 769 (observed 2026-08-28T04:09:13.725476+00:00)

## What it is
A curated awesome-list of MLOps tools, covering categories like AutoML, CI/CD for ML, feature stores, model serving, drift detection, and workflow orchestration. It also links to articles, books, podcasts, and other resources for machine learning engineering.

## Use cases
- find mlops tools for deploying machine learning models
- discover feature store and data validation tools
- learn about machine learning operations best practices
- compare automl and hyperparameter tuning tools
- find resources like books and podcasts on mlops
- find tools for model monitoring and drift detection

## When to choose
- you want a broad, curated overview of the MLOps tooling ecosystem
- you are evaluating tools for a specific ML lifecycle stage like serving or monitoring
- you want learning resources such as books and podcasts on MLOps

## When to avoid
- you need a working tool rather than a list of links
- you need detailed comparisons or benchmarks of the listed tools
- you need an actively maintained software dependency in your project

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, machine-learning, ci-cd, monitoring, data-science
- domain: machine-learning, data-science, awesome-lists
- platform: python, cross-platform
- tags: awesome-list, mlops, curated-list, model-deployment, feature-store, automl, model-monitoring, devops

## Member repositories
- kelvins/awesome-mlops (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:13.725476+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 2026-08-29T17:59:32.874671+00:00, confidence not recorded.
  - readme: https://github.com/kelvins/awesome-mlops (fetched 2026-08-28T04:09:13.725476+00:00, sha 6a3903b0fb41)
- Data as of 2026-08-30T08:39:29.467469+00:00.