Alternatives and similar repositories for Ethical-AI-Standard

Users that are interested in Ethical-AI-Standard are comparing it to the libraries listed below

• hanneshapke / Deploying-Deep-Learning-Models
  Strategies to deploy deep learning models
  ☆27Updated 7 years ago
• jphall663 / secure_ML_ideas
  Practical ideas on securing machine learning models
  ☆36Updated 4 years ago
• zelros / cinnamon
  CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system
  ☆77Updated 2 years ago
• EricSchles / drifter_ml
  A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.
  ☆104Updated 4 years ago
• dkn22 / embedder
  Embed categorical variables via neural networks.
  ☆59Updated 2 years ago
• aredier / trelawney
  General Interpretability Package
  ☆58Updated 2 years ago
• supsi-dacd-isaac / mbtr
  Multivariate Boosted TRee
  ☆63Updated 3 years ago
• aromain / intermarche_challenge
  [Intemarché] Sales forecasting challenge
  ☆11Updated 4 years ago
• hypergol / hypergol
  Hypergol is a Data Science/Machine Learning productivity toolkit to accelerate any projects into production with autogenerated code, stan…
  ☆53Updated 2 years ago
• HazyResearch / reef
  Automatically labeling training data
  ☆107Updated 6 years ago
• MonaNashaat / Asterisk
  Generating Training Data Made Easy
  ☆43Updated 5 years ago
• koaning / scikit-fairness
  this repo might get accepted
  ☆28Updated 4 years ago
• EthicalML / explainability-and-bias
  ☆103Updated 2 years ago
• cosmicBboy / themis-ml
  A library that implements fairness-aware machine learning algorithms
  ☆127Updated 5 years ago
• pcerda / string_categorical_encoders
  Scripts for paper "Encoding high-cardinality string categorical variables"
  ☆24Updated 6 years ago
• dtriepke / nlp_model_selection_app
  ☆16Updated 4 years ago
• klemag / PyconUS_2019-model-interpretability-tutorial
  Content for the Model Interpretability Tutorial at Pycon US 2019
  ☆41Updated last year
• ottogroup / dstoolbox
  Tools that make working with scikit-learn and pandas easier.
  ☆44Updated 2 months ago
• LDSSA / heroku-model-deploy
  Deploy a scikit model using heroku and Flask
  ☆15Updated 2 years ago
• metriculous-ml / metriculous
  Measure and visualize machine learning model performance without the usual boilerplate.
  ☆99Updated last year
• fastai / ethics.fast.ai
  The fast.ai data ethics course
  ☆17Updated 2 years ago
• muellerzr / fastai2-Tabular-Baselines
  A few baselines with a standard tabular model
  ☆38Updated 5 years ago
• Automunge / AutoMunge
  Tabular feature encoding pipelines for machine learning with options for string parsing, missing data infill, and stochastic perturbation…
  ☆164Updated 4 months ago
• janfreyberg / superintendent
  Practical active learning in python
  ☆190Updated 3 years ago
• yeounoh / slicefinder
  automatic data slicing
  ☆34Updated 4 years ago
• Machine-Learning-Tokyo / ML_Fairness_Ethics_Explainability
  Fairness, Ethics, Explainability in AI and ML
  ☆22Updated 5 years ago
• MaxHalford / naked
  The simplest way to deploy a machine learning model
  ☆24Updated 2 years ago
• jmzhao / pbos
  ☆19Updated 5 years ago
• Palashio / nylon
  An intelligent, flexible grammar of machine learning.
  ☆82Updated 4 years ago
• peopledoc / mlvtools-tutorial
  Tutorial for a new versioning Machine Learning pipeline
  ☆80Updated 4 years ago