# Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies

This repository contains a curated collection of 300+ case studies from over 80 companies, detailing practical applications and insights into machine learning (ML) system design. The contents are organized to help you easily find relevant case studies based on industry or specific ML use cases.

Repository: https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies
Canonical: https://ross.abutalabs.com/products/a-curated-list-of-ml-system-design-case-studies
License Family: other
Last push: 2025-08-05T04:07:00+00:00

## Health v2 (maintenance only)
Score: 40/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 35, release rhythm 35, longevity 59
- inputs: {"age_days": 837, "days_push": 393, "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 10958, forks 1681 (observed 2026-08-28T04:10:44.836600+00:00)

## What it is
A curated repository of 300+ machine learning system design case studies from over 80 companies such as Netflix, Airbnb, and DoorDash. It organizes real-world ML applications by industry and use case to help readers learn how ML systems are designed in production.

## Use cases
- learn ml system design from real company case studies
- prepare for machine learning system design interviews
- find examples of recommender systems and fraud detection in production
- study how netflix airbnb and doordash use machine learning
- browse curated ml case studies by industry

## When to choose
- you want real-world production ML system design examples from major companies
- you are preparing for ML system design interviews and need concrete case studies
- you want a free, organized index of ML applications across industries

## When to avoid
- you need runnable code or a software library rather than reading material
- you want step-by-step tutorials with hands-on exercises instead of case study write-ups
- you need up-to-date documentation with a maintained license and formal releases

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools, documentation
- domain: machine-learning, tutorials, awesome-lists, artificial-intelligence, data-science
- platform: -
- tags: case-studies, ml-system-design, curated-list, interview-prep, industry-examples, recommender-systems, fraud-detection, search-ranking, web-server

## Member repositories
- Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies (main) score 40

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.836600+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 2026-08-29T17:17:17.693064+00:00, confidence not recorded.
  - readme: https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies (fetched 2026-08-28T04:10:44.836600+00:00, sha 8f5aed3e32ed)
- Data as of 2026-08-30T08:39:29.467469+00:00.