awesome-production-machine-learning VS shap

Compare awesome-production-machine-learning vs shap and see what are their differences.

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning (by EthicalML)
Machine Learning Mlops Interpretability explainability responsible-ai Deep Learning machine-learning-operations ml-ops ml-operations privacy-preserving privacy-preserving-ml privacy-preserving-machine-learning Data Mining large-scale-ml production-ml large-scale-machine-learning production-machine-learning Awesome awesome-list
Source Code
ethical.institute
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shap

A game theoretic approach to explain the output of any machine learning model. (by shap)
Interpretability Machine Learning Deep Learning gradient-boosting Shap Shapley explainability
Source Code
shap.readthedocs.io
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awesome-production-machine-learning shap
9 38
15,947 21,632
2.1% 2.0%
7.4 9.3
8 days ago 5 days ago
Jupyter Notebook
MIT License 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.

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.

shap

Posts with mentions or reviews of shap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-06.

What are some alternatives?

When comparing awesome-production-machine-learning and shap you can also consider the following projects:

awesome-jax - JAX - A curated list of resources https://github.com/google/jax

shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

netron - Visualizer for neural network, deep learning and machine learning models

Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

awesome-mlops - :sunglasses: A curated list of awesome MLOps tools

captum - Model interpretability and understanding for PyTorch

awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security

lime - Lime: Explaining the predictions of any machine learning classifier

datascience - Curated list of Python resources for data science.

interpret - Fit interpretable models. Explain blackbox machine learning.

awesome-ocr

anchor - Code for "High-Precision Model-Agnostic Explanations" paper

awesome-production-machine-learning vs awesome-jax shap vs shapash awesome-production-machine-learning vs netron shap vs Transformer-Explainability awesome-production-machine-learning vs awesome-mlops shap vs captum awesome-production-machine-learning vs awesome-ml-for-cybersecurity shap vs lime awesome-production-machine-learning vs datascience shap vs interpret awesome-production-machine-learning vs awesome-ocr shap vs anchor