applied-ml
A curated list of papers and tech blog posts about using machine learning in real production systems.
- Stars
- 30.8k
- Forks
- 4k
- Licence
- MIT
- Language
- —
- Last update
- 2.3 years ago
- Type
- Curated list
No commits in over a year — check for forks or alternatives before adopting.
What it is
applied-ml is a reading list of papers, articles, and blog posts in which companies describe how they built and ran data science and machine learning systems in production. Entries are grouped by topic such as data quality, feature stores, recommendation, search, and MLOps, and each one is tagged with the company and year. It is meant to show how other organizations framed a problem, which techniques worked or did not, and what results they reported.
- Browse the Recommendation or Search & Ranking sections to see how companies framed those problems
- Read the Data Quality entries to learn how teams monitored and validated training data
- Compare several Data Discovery posts to understand different metadata and catalog approaches
- Use the list as a starting point for literature review before writing a design document
Great for
- Engineers planning an ML project who want to see how other companies approached a similar problem
- Data scientists looking for real-world case studies on recommendation, search, or data quality
- Students and researchers who want practical context alongside academic papers
- Team leads comparing infrastructure choices such as feature stores or data pipelines
Not ideal for
- People who want runnable code or a software tool, since this repository is a list of links and does not install anything
- Readers looking for an introductory machine learning course, because the entries assume some working knowledge
- Anyone needing up-to-date coverage, since the README shows a last push in July 2024 and the visible entries are older
How to get it
- 1
Read it online
Clone the repository to read it offline
git clone https://github.com/eugeneyan/applied-ml - 2
Run it
- Open the README on GitHub and read the table of contents.
- Pick one topic section, such as Recommendation or Data Quality.
- Open a linked article and note the problem framing, techniques, and reported results.
- Compare two entries from different companies on the same topic.
- 3
Get the most out of it
- Follow the linked P.S. and P.P.S. resources for ML survey and applied ML guide material mentioned in the README.
- Read the Paper links alongside company blog posts to connect practice with research.
- Check the year tag on each entry to prioritize more recent work.
Downloads
Strengths
- Curated by topic, so it is easy to jump to a relevant area
- Each entry lists the company and year, which helps judge how current it is
- Links to both industry write-ups and academic papers in many cases
Trade-offs
- It is a list of links, so the quality and availability of each linked article depends on its host
- The visible README is truncated, so the full set of topics and entries could not be fully checked here
- Several entries are older, and the list does not summarize results itself, so readers must read each source
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| applied-ml (this) | 30.8k | Curated list | beginner | 2.3 years ago | Engineers planning an ML project who want to see how other companies approached a similar problem |
| awesome-production-machine-learning | 21.1k | Curated list | beginner | 5 days ago | ML engineers looking for tools to deploy or monitor models |
| Made-With-ML | 49.9k | Learning resource | intermediate | 8 months ago | Software engineers who want to learn how ML models get deployed and maintained |
| wandb | 11.3k | Library | beginner | 4 days ago | Machine learning engineers who want to compare training runs over time |
| awesome-artificial-intelligence | 16.8k | Curated list | beginner | 3 months ago | Software developers who want to learn the foundations behind language models and agents |
| annotated_deep_learning_paper_implementations | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.