# EthicalML/awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Repository: https://github.com/EthicalML/awesome-production-machine-learning
Canonical: https://ross.abutalabs.com/products/awesome-production-machine-learning
Homepage: https://ethicalml.github.io/awesome-production-machine-learning
License: MIT
License Family: permissive
Topics: machine-learning, mlops, interpretability, explainability, responsible-ai, deep-learning, machine-learning-operations, ml-ops, ml-operations, privacy-preserving, privacy-preserving-ml, privacy-preserving-machine-learning, data-mining, large-scale-ml, production-ml, large-scale-machine-learning, production-machine-learning, awesome, awesome-list
Last push: 2026-08-26T05:40:57+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 95, longevity 100
- inputs: {"age_days": 2940, "days_push": 7, "days_rel": 32, "gap_med": 30.0, "n_releases_24m": 21}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 20868, forks 2592 (observed 2026-08-28T04:11:30.462382+00:00)

## What it is
A curated awesome-list of open source libraries for deploying, monitoring, versioning, scaling, and securing machine learning in production. It covers the full MLOps toolchain, from data pipelines and feature stores to explainability, privacy, and model serving.

## Use cases
- find open source tools for deploying machine learning models to production
- discover mlops libraries for monitoring and versioning models
- curated list of production machine learning tooling
- find libraries for model explainability and fairness
- explore privacy-preserving machine learning tools
- compare feature stores and data pipeline tools for ml
- learn about the mlops ecosystem and toolchain

## When to choose
- you want a broad, community-curated map of the MLOps landscape before picking tools
- you need recommendations across the full production ML lifecycle, from data to serving to monitoring
- you want regularly updated coverage of new production ML libraries

## When to avoid
- you need a working tool rather than a directory of links
- you want in-depth tutorials or implementation guidance rather than a catalog
- you need vendor-specific or commercial MLOps platform comparisons

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, monitoring, deployment, workflow-automation, developer-tools
- domain: machine-learning, data-science, awesome-lists, artificial-intelligence
- platform: cross-platform
- tags: awesome-list, mlops, curated-list, production-ml, explainability, privacy-preserving-ml, model-serving, model-monitoring

## Member repositories
- EthicalML/awesome-production-machine-learning (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:30.462382+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 2026-08-29T16:58:31.969060+00:00, confidence not recorded.
  - readme: https://github.com/EthicalML/awesome-production-machine-learning (fetched 2026-08-28T04:11:30.462382+00:00, sha df5c52b5b410)
  - homepage: https://ethicalml.github.io/awesome-production-machine-learning (fetched 2026-08-29T07:57:25.501301+00:00, sha d64da3cfd6b8)
- Data as of 2026-08-30T08:39:29.467469+00:00.