# SE-ML/awesome-seml

A curated list of articles that cover the software engineering best practices for building machine learning applications.

Repository: https://github.com/SE-ML/awesome-seml
Canonical: https://ross.abutalabs.com/products/awesome-seml
License: CC0-1.0
License Family: permissive
Topics: awesome, awesome-list, software-engineering, machine-learning, deep-learning, ml-ops
Last push: 2024-03-26T22:53:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2408, "days_push": 890, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1367, forks 124 (observed 2026-08-28T04:04:31.664190+00:00)

## What it is
A curated list of articles, papers, and guides covering software engineering best practices for building machine learning applications, spanning data management, model training, deployment, governance, and team collaboration. It accompanies a survey on the adoption of these practices in industry.

## Use cases
- find best practices for building production ML applications
- learn MLOps and software engineering for machine learning
- find must-read papers on ML technical debt and engineering
- learn how to manage datasets and data quality for ML
- find resources on testing and versioning ML models
- onboard a team to ML engineering practices

## When to choose
- you want a curated, categorized reading list on ML engineering practices
- you need academic papers and industry guides on MLOps topics
- you are setting up team processes for ML development

## When to avoid
- you need runnable tools or code rather than articles and papers
- you want tutorials on ML algorithms themselves rather than surrounding engineering
- you need an up-to-date tooling directory rather than conceptual guidance

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: machine-learning, awesome-lists, developer-tools
- platform: -
- tags: awesome-list, mlops, curated-list, best-practices, software-engineering, ml-engineering, devops, web-server

## Member repositories
- SE-ML/awesome-seml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:31.664190+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 2026-08-30T04:41:06.187879+00:00, confidence not recorded.
  - readme: https://github.com/SE-ML/awesome-seml (fetched 2026-08-28T04:04:31.664190+00:00, sha dd43d19ee48a)
- Data as of 2026-08-30T08:39:29.467469+00:00.