linkedin / LiFTLinks
The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.
☆172Updated 2 years ago
Alternatives and similar repositories for LiFT
Users that are interested in LiFT are comparing it to the libraries listed below
Sorting:
- Tabular feature encoding pipelines for machine learning with options for string parsing, missing data infill, and stochastic perturbation…☆165Updated 3 weeks ago
- A toolkit that streamlines and automates the generation of model cards☆436Updated 2 years ago
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆353Updated last week
- ☆102Updated 2 years ago
- Coarse-grained lineage and tracing for machine learning pipelines.☆470Updated 2 years ago
- Toy example of an applied ML pipeline for me to experiment with MLOps tools.☆209Updated 3 years ago
- A library that implements fairness-aware machine learning algorithms☆126Updated 4 years ago
- ☆96Updated 5 years ago
- 🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.☆182Updated last year
- Practical ideas on securing machine learning models☆36Updated 4 years ago
- Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshop…☆144Updated 11 months ago
- 🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects☆81Updated 3 years ago
- Data Analysis Baseline Library☆133Updated 9 months ago
- A Collection of GitHub Actions That Facilitate MLOps☆208Updated 2 years ago
- Recipes for Driverless AI☆251Updated 2 weeks ago
- 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.☆148Updated last year
- detect demographic differences in the output of machine learning models or other assessments☆317Updated 5 years ago
- Concept drift monitoring for HA model servers.☆102Updated 2 years ago
- A deep ranking personalization framework☆134Updated 2 years ago
- Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!☆28Updated 6 years ago
- A collection of machine learning model cards and datasheets.☆77Updated 2 months ago
- ☆54Updated 2 years ago
- The fast.ai data ethics course☆16Updated 2 years ago
- Library for Semi-Automated Data Science☆339Updated 2 months ago
- 🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.☆264Updated 2 years ago
- Tutorial for a new versioning Machine Learning pipeline☆80Updated 4 years ago
- Spark implementation of computing Shapley Values using monte-carlo approximation☆75Updated 2 years ago
- A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.☆102Updated 3 years ago
- A flexible neural network framework for running experiments and trying ideas.☆81Updated 5 years ago
- Code accompanying the "Debugging machine learning in production" talk☆30Updated 3 years ago