# visenger/awesome-mlops

A curated list of references for MLOps

Repository: https://github.com/visenger/awesome-mlops
Canonical: https://ross.abutalabs.com/products/awesome-mlops
Homepage: https://ml-ops.org
License Family: other
Topics: machine-learning, mlops, data-science, engineering, federated-learning, devops, software-engineering, ai, ml
Last push: 2024-11-21T14:45:11+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2374, "days_push": 650, "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 14165, forks 2107 (observed 2026-08-28T04:11:05.694610+00:00)

## What it is
A curated awesome list of references, tools, books, papers, and courses for MLOps (Machine Learning Operations). It covers the full ML lifecycle including data engineering, deployment, testing, monitoring, and governance.

## Use cases
- find resources for putting machine learning models into production
- learn mlops best practices and workflows
- discover tools for ml model deployment and monitoring
- find books and papers about machine learning operations
- learn about feature stores and data engineering for ml
- explore ml model governance and responsible ai resources

## When to choose
- you want a broad curated starting point for learning MLOps
- you need references across the whole ML lifecycle from data to deployment
- you are building a learning path for ML engineering teams

## When to avoid
- you need a runnable tool or framework rather than a reference list
- you need up-to-date tooling recommendations with hands-on support
- you want a structured course rather than a link collection

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, documentation
- domain: machine-learning, data-science, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, mlops, curated-resources, machine-learning-operations, reference, devops

## Member repositories
- visenger/awesome-mlops (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:05.694610+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 2026-08-29T17:12:46.026432+00:00, confidence not recorded.
  - readme: https://github.com/visenger/awesome-mlops (fetched 2026-08-28T04:11:05.694610+00:00, sha 1e9619dd2678)
  - homepage: https://ml-ops.org (fetched 2026-08-29T08:06:42.038951+00:00, sha a573b772fb3a)
- Data as of 2026-08-30T08:39:29.467469+00:00.