# eugeneyan/applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Repository: https://github.com/eugeneyan/applied-ml
Canonical: https://ross.abutalabs.com/products/applied-ml
License: MIT
License Family: permissive
Topics: applied-machine-learning, production, applied-data-science, machine-learning, data-science, reinforcement-learning, data-engineering, recsys, search, deep-learning, data-quality, data-discovery, computer-vision, natural-language-processing
Last push: 2024-07-18T22:41:02+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": 2251, "days_push": 776, "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 30087, forks 3991 (observed 2026-08-28T04:11:53.826307+00:00)

## What it is
A curated collection of papers and tech blog posts from companies describing how they apply data science and machine learning in production. It organizes real-world case studies by topic such as data quality, recommendation, search, NLP, computer vision, and MLOps.

## Use cases
- find case studies of machine learning in production
- learn how companies implement recommendation systems
- research MLOps practices at large tech companies
- find papers on applied data science techniques
- understand how to frame and deploy ML projects
- study real-world results and ROI of ML systems

## When to choose
- you want industry case studies and production experience rather than academic theory
- you are designing an ML system and want to learn from companies like Airbnb, Uber, and Google
- you need a curated reading list spanning data engineering to MLOps

## When to avoid
- you need runnable code or a software library
- you want step-by-step tutorials or beginner courses
- you need up-to-date coverage of the newest techniques, as entries are dated company blog posts

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, documentation
- domain: machine-learning, data-science, tutorials, awesome-lists
- platform: -
- tags: curated-list, papers, production-ml, mlops, case-studies, tech-blogs, data-engineering, web-server

## Member repositories
- eugeneyan/applied-ml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:53.826307+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 2026-08-29T16:52:48.129183+00:00, confidence not recorded.
  - readme: https://github.com/eugeneyan/applied-ml (fetched 2026-08-28T04:11:53.826307+00:00, sha a5a9c61283ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.