awesome-seml
A curated list of articles that cover the software engineering best practices for building machine learning applications. (by SE-ML)
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning (by EthicalML)
| awesome-seml | awesome-production-machine-learning | |
|---|---|---|
| 1 | 9 | |
| 1,195 | 16,020 | |
| 0.9% | 1.1% | |
| 0.0 | 7.5 | |
| about 1 month ago | 1 day ago | |
| Creative Commons Zero v1.0 Universal | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-seml
Posts with mentions or reviews of awesome-seml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-16.
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[D] How to maintain ML models?
They also have an awesome-seml repo on GitHub outlining many (scientific) articles as well as tools and frameworks that may help you out in implementing these best practices.
awesome-production-machine-learning
Posts with mentions or reviews of awesome-production-machine-learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-13.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning
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How much of a full DS project pipeline can I do for free?
There are a lot of frameworks and specific tools out there that try to make production ML projects viable; from specific like Airflow (orchestrating jobs) and MLflow (experiment tracking) to more complex ones like Kubeflow. You can have a grasp here.
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Sqldiff: SQLite Database Difference Utility
https://github.com/EthicalML/awesome-production-machine-lear...
- [D] What are the best resources to crack M L system design interviews?
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I'm looking for a tool that let's you visualize the models architecture like this. Any idea what it is called?
https://github.com/EthicalML/awesome-production-machine-learning I think you will find most of the tools to visualize the model on this link.
- Awesome production machine learning - curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning [free] [website] [@all]
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Crucial differences in MLOps for deep learning
2/ https://github.com/EthicalML/awesome-production-machine-learning