awesome-production-machine-learning
awesome-jax
Our great sponsors
| awesome-production-machine-learning | awesome-jax | |
|---|---|---|
| 9 | 3 | |
| 15,947 | 1,288 | |
| 2.1% | - | |
| 7.4 | 6.2 | |
| 7 days ago | 8 days ago | |
| MIT License | Creative Commons Zero v1.0 Universal |
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-production-machine-learning
-
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.
-
[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
-
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.
-
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?
-
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]
-
Crucial differences in MLOps for deep learning
2/ https://github.com/EthicalML/awesome-production-machine-learning
awesome-jax
-
[D] Any less-boilerplate framework for Jax/Flax/Haiku?
Have you looked in here?
-
[D] JAX learning resources?
Here's a compilation of resources: https://github.com/n2cholas/awesome-jax
What are some alternatives?
shap - A game theoretic approach to explain the output of any machine learning model.
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
netron - Visualizer for neural network, deep learning and machine learning models
awesome-ocr
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
awesome-ai-in-finance - 🔬 A curated list of awesome LLMs & deep learning strategies & tools in financial market.
awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
datascience - Curated list of Python resources for data science.
awesome-deep-learning - A curated list of awesome Deep Learning tutorials, projects and communities.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration