This collection links community detection research papers and their implementations.
Open source
24 alternativesApplied ML
This repository collects company papers and technical blogs about machine learning in production.
About Applied ML
Applied ML is an open-source GitHub repository that curates papers, articles, and engineering blogs in which companies describe their data science and machine learning work in production. Entries are grouped by topic, with each item naming the organization and the year of publication, so readers can trace how a problem was framed, which techniques were used, and what results were reported.
The collection covers areas such as data quality, data engineering, data discovery, feature stores, classification, regression, forecasting, recommendation, search and ranking, embeddings, natural language processing, sequence modelling, computer vision, reinforcement learning, anomaly detection, graphs, optimization, information extraction, weak supervision, generation, audio, privacy-preserving machine learning, validation and A/B testing, model management, efficiency, ethics, infrastructure, MLOps platforms, practices, team structure, and failures. The repository is a reading resource rather than software to install; the linked material comes from third parties, is written in English, and pricing is not stated.
Key features
- Curated papers, articles, and blogs on production data science and machine learning
- Entries list the publishing organization and publication year
- Table of contents organized by topic, from data quality to model management
- Covers problem framing, techniques used, and reported real-world results
- Notes cases where approaches did not work
- Links to related repositories ml-surveys and applyingML
Best for: Data scientists, ML engineers, and technical leads who want company accounts of how production machine learning problems were approached and what happened.
Good for
- Organized index spanning many production ML topics and companies
- Each entry cites the organization and year, making sources easy to trace
- Open repository with a CONTRIBUTING.md file for additions
Watch out for
- A curated reading list rather than a tool or code you can run
- Content is in English; pricing is unknown
Applied ML at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Applied ML FAQ
What does the repository contain?
It is a curated collection of papers, articles, and blogs from companies sharing their work on data science and machine learning in production.
What topics does it cover?
Its table of contents spans data quality, data engineering, data discovery, feature stores, classification, regression, forecasting, recommendation, search and ranking, embeddings, NLP, sequence modelling, computer vision, reinforcement learning, anomaly detection, graphs, optimization, information extraction, weak supervision, generation, audio, privacy-preserving machine learning, validation and A/B testing, model management, efficiency, ethics, infrastructure, MLOps platforms, practices, team structure, and failures.
Does it describe techniques that did not work?
The repository states that it covers what machine learning techniques worked and sometimes what did not, along with the reasoning behind them and the real-world results achieved.
Is something wrong or out of date?
Tell us and an editor will check it.
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