applied-ml vs awesome-production-machine-learning
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A curated list of papers and tech blog posts about using machine learning in real production systems.
Open source · MIT
A curated list of open source libraries for deploying, monitoring, versioning and scaling machine learning in
The short answer
Choose applied-ml if you need…
- 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
Choose awesome-production-machine-learning if you need…
- ML engineers looking for tools to deploy or monitor models
- Data teams scouting options for model versioning, experiment tracking or feature stores
- Engineering leads building a production ML stack who want a broad overview
- Researchers or students wanting to see what the production ML ecosystem includes
Side-by-side
| applied-ml | awesome-production-machine-learning | |
|---|---|---|
| Type | Curated list | Curated list |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 30.8k | 21.1k |
| Setup difficulty | Beginner | Beginner |
| How you get it | Read it online | Read it online |
| Main language | — | — |
| Last code update | 2 years ago | 5 days ago |
What each one is
applied-ml
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.
awesome-production-machine-learning
This is a categorized list of open source tools that help teams take machine learning models from experiments into real production use. It covers areas such as AutoML, deployment and serving, monitoring, feature stores, and privacy and safety, with each entry linking to its project page. People use it as a starting point for finding and comparing tools rather than as software to install.
Best for
applied-ml
- 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
awesome-production-machine-learning
- ML engineers looking for tools to deploy or monitor models
- Data teams scouting options for model versioning, experiment tracking or feature stores
- Engineering leads building a production ML stack who want a broad overview
- Researchers or students wanting to see what the production ML ecosystem includes
Strengths
applied-ml
- 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
awesome-production-machine-learning
- Broad coverage across the full production ML lifecycle in one organized place
- Sections are grouped by function, with a quick-links table for navigation
- Maintained with monthly release summaries and a contribution process
Not ideal for
applied-ml
- 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
awesome-production-machine-learning
- People who want a single ready-to-run application, since this is only a list of other projects
- Beginners without ML background, because the entries assume familiarity with terms like feature stores and hyperparameter tuning
- Anyone needing a verified recommendation, since the README does not rank or test the listed projects
Trade-offs
applied-ml
- 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
awesome-production-machine-learning
- It is a list, so each tool still needs separate evaluation for fit, maturity and maintenance
- The README excerpt provided is truncated, so not every section could be reviewed
Getting started
applied-ml
Clone the repository to read it offline
git clone https://github.com/eugeneyan/applied-mlawesome-production-machine-learning
Clone the repository locally (optional)
git clone https://github.com/EthicalML/awesome-production-machine-learningFrequently asked questions
What is the difference between applied-ml and awesome-production-machine-learning?
applied-ml: A curated list of papers and tech blog posts about using machine learning in real production systems. awesome-production-machine-learning: A curated list of open source libraries for deploying, monitoring, versioning and scaling machine learning in . applied-ml suits engineers planning an ml project who want to see how other companies approached a similar problem, while awesome-production-machine-learning suits ml engineers looking for tools to deploy or monitor models.
Are applied-ml and awesome-production-machine-learning free?
applied-ml: Free and open source. awesome-production-machine-learning: Free and open source.
Is applied-ml or awesome-production-machine-learning more popular?
applied-ml has more GitHub stars (30.8k) than awesome-production-machine-learning (21.1k). Popularity isn't the same as fit — check which one matches your use case above.
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.