grape
awesome-production-machine-learning
| grape | awesome-production-machine-learning | |
|---|---|---|
| 3 | 9 | |
| 482 | 16,020 | |
| 2.9% | 1.1% | |
| 6.4 | 7.5 | |
| 2 months ago | 2 days ago | |
| Jupyter Notebook | ||
| MIT License | MIT License |
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grape
- Grape (Graph Representation LeArning, Predictions and Evaluation)
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Zoomable, animated scatterplots in the browser that scales over a billion points
Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.
Visualizing the edges at scale doesnt yield nice results in general.
The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.
My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).
Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.
[1] https://github.com/VHRanger/nodevectors
[2] https://github.com/AnacletoLAB/grape/
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
awesome-production-machine-learning
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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
What are some alternatives?
deodel - A mixed attributes predictive algorithm implemented in Python.
shap - A game theoretic approach to explain the output of any machine learning model.
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
deepscatter - Zoomable, animated scatterplots in the browser that scales over a billion points
netron - Visualizer for neural network, deep learning and machine learning models
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
nanocube
awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
datascience - Curated list of Python resources for data science.