linkedin / LiFTLinks
The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.
☆171Updated 2 years ago
Alternatives and similar repositories for LiFT
Users that are interested in LiFT are comparing it to the libraries listed below
Sorting:
- ☆102Updated 2 years ago
- Tabular feature encoding pipelines for machine learning with options for string parsing, missing data infill, and stochastic perturbation…☆165Updated last month
- A toolkit that streamlines and automates the generation of model cards☆437Updated 2 years ago
- Toy example of an applied ML pipeline for me to experiment with MLOps tools.☆208Updated 3 years ago
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆352Updated 3 weeks ago
- Coarse-grained lineage and tracing for machine learning pipelines.☆470Updated 2 years ago
- Library for Semi-Automated Data Science☆342Updated 3 months ago
- A collection of machine learning model cards and datasheets.☆77Updated 2 weeks ago
- Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshop…☆144Updated last year
- A library that implements fairness-aware machine learning algorithms☆126Updated 4 years ago
- A machine learning testing framework for sklearn and pandas. The goal is to help folks assess whether things have changed over time.☆103Updated 3 years ago
- Recipes for Driverless AI☆251Updated last month
- detect demographic differences in the output of machine learning models or other assessments☆318Updated 5 years ago
- 🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects☆82Updated 3 years ago
- Data Analysis Baseline Library☆133Updated 10 months ago
- A Collection of GitHub Actions That Facilitate MLOps☆208Updated 2 years ago
- The fast.ai data ethics course☆17Updated 2 years ago
- ☆96Updated 5 years ago
- The Data Linter identifies potential issues (lints) in your ML training data.☆88Updated 7 years ago
- ForML - A development framework and MLOps platform for the lifecycle management of data science projects☆106Updated 2 years ago
- Concept drift monitoring for HA model servers.☆102Updated 2 years ago
- Practical ideas on securing machine learning models☆36Updated 4 years ago
- Spark implementation of computing Shapley Values using monte-carlo approximation☆76Updated 2 years ago
- 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.☆148Updated last year
- Code accompanying the "Debugging machine learning in production" talk☆30Updated 3 years ago
- 🎯 kettle is a CLI tool for creating and deploying cloud functions & docker containers for machine learning☆32Updated 2 years ago
- Natural language processing support for Pandas dataframes.☆216Updated 5 months ago
- 🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.☆182Updated last year
- A deep ranking personalization framework☆133Updated 2 years ago
- A flexible neural network framework for running experiments and trying ideas.☆81Updated 5 years ago