This repository offers a curated collection of over 300 machine learning (ML) system design case studies from more than 80 companies, targeting ML engineers, researchers, and practitioners. It provides practical insights into real-world ML applications across various industries and use cases, enabling users to learn from established companies' experiences.

How It Works

The repository organizes case studies by industry and ML application type, including computer vision, NLP, recommender systems, and fraud detection. Each entry links to detailed articles, blogs, or papers, offering firsthand accounts of deployed ML systems, their design choices, evaluation criteria, and implementation architectures.

Quick Start & Requirements

No installation is required; this is a curated list of external resources. Access to the linked articles and case studies is necessary.

Highlighted Details

• Features case studies from industry leaders like Netflix, Airbnb, Uber, and Google.
• Covers a wide spectrum of ML applications, from recommender systems and fraud detection to NLP and computer vision.
• Includes insights into specific product features powered by ML, such as personalized recommendations and demand forecasting.
• Many entries are from 2022-2023, reflecting recent advancements and practices.

Maintenance & Community

This is a static, curated list. The repository is maintained by Engineer1999. There are no explicit community links or roadmaps provided.

Licensing & Compatibility

The repository itself is licensed under the MIT License, permitting commercial use and modification. However, users must adhere to the licensing terms of the individual external resources linked within the repository.

Limitations & Caveats

The repository is a collection of links, and the availability or accuracy of the external content is not guaranteed by the repository maintainer. Some links may become outdated or inaccessible over time.