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EthicalML / awesome-production-machine-learning

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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

machine-learning data-mining awesome deep-learning awesome-list interpretability privacy-preserving production-machine-learning mlops privacy-preserving-machine-learning explainability responsible-ai machine-learning-operations ml-ops ml-operations privacy-preserving-ml large-scale-ml production-ml large-scale-machine-learning
  • Updated Jun 4, 2021

h1st-ai / h1st

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The AI Application Platform We All Need. Human AND Machine Intelligence. Based on experience building AI solutions at Panasonic: robotics predictive maintenance, cold-chain energy optimization, Gigafactory battery mfg, avionics, automotive cybersecurity, and more.

home-automation data-science time-series collaboration cybersecurity cold-start autonomous-vehicles hacktoberfest automl avionics human-in-the-loop predictive-maintenance ensemble-machine-learning datascience-environment explainability industrial-iot trustworthy-datascience energy-optimization
  • Updated Jun 5, 2021
  • Jupyter Notebook

microsoft / responsible-ai-widgets

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[Error Analysis] Support for bringing in features that are not being used by the model but useful for error analysis

nushib
nushib commented Jun 2, 2021

Suggested by Melanie Fernandez Pradier:

“Given a model trained on certain features, is there any way I can include additional features (not used in training) but that I want to monitor in the error analysis?"

This is currently possible by enriching the set of input features to the dashboard after the inference step. However, will need further support on the UI side to clearly mark features t

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Design Error Analysis documentation enhancement
hierarchical-dnn-interpretations

csinva / hierarchical-dnn-interpretations

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Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

python data-science machine-learning statistics deep-neural-networks ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-networks acd interpretation iclr interpretability feature-importance explainable-ai explainability
  • Updated Mar 14, 2021
  • Jupyter Notebook
deep-explanation-penalization

laura-rieger / deep-explanation-penalization

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Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

python data-science machine-learning ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-network fairness interpretability cdep feature-importance recurrent-neural-network interpretable-deep-learning explainable-ai explainability fairness-ml
  • Updated Mar 22, 2021
  • Jupyter Notebook

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