linkedin / LiFT
The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.
☆171Updated last year
Alternatives and similar repositories for LiFT:
Users that are interested in LiFT are comparing it to the libraries listed below
- A toolkit that streamlines and automates the generation of model cards☆432Updated last year
- A library that implements fairness-aware machine learning algorithms☆124Updated 4 years ago
- Tabular feature encoding pipelines for machine learning with options for string parsing, missing data infill, and stochastic perturbation…☆165Updated 3 months ago
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆349Updated this week
- Toy example of an applied ML pipeline for me to experiment with MLOps tools.☆208Updated 3 years ago
- Practical ideas on securing machine learning models☆36Updated 3 years ago
- Coarse-grained lineage and tracing for machine learning pipelines.☆469Updated 2 years ago
- A Collection of GitHub Actions That Facilitate MLOps☆207Updated 2 years ago
- A collection of Machine Learning examples to get started with deploying RAPIDS in the Cloud☆141Updated 6 months ago
- A collection of machine learning model cards and datasheets.☆75Updated 10 months ago
- 🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.☆263Updated 2 years ago
- O'Reilly Katacoda☆56Updated 2 years ago
- Spark implementation of computing Shapley Values using monte-carlo approximation☆74Updated 2 years ago
- ☆102Updated last year
- Data Analysis Baseline Library☆131Updated 6 months ago
- 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.☆146Updated last year
- Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world d…☆428Updated 2 months ago
- The fast.ai data ethics course☆16Updated 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
- ☆43Updated 2 years ago
- 🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.☆182Updated 9 months ago
- A deep ranking personalization framework☆134Updated last year
- Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshop…☆142Updated 8 months ago
- A flexible neural network framework for running experiments and trying ideas.☆81Updated 5 years ago
- ☆86Updated 6 years ago
- Concept drift monitoring for HA model servers.☆101Updated 2 years ago
- ☆26Updated 4 years ago
- MLOps pipeline for NVIDIA Merlin on GKE☆41Updated 3 years ago
- Content for the Model Interpretability Tutorial at Pycon US 2019☆41Updated 9 months ago
- Project for open sourcing research efforts on Backward Compatibility in Machine Learning☆73Updated last year