microsoft / responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
☆1,527Updated 3 months ago
Alternatives and similar repositories for responsible-ai-toolbox:
Users that are interested in responsible-ai-toolbox are comparing it to the libraries listed below
- Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world d…☆428Updated 3 months ago
- A Python package to assess and improve fairness of machine learning models.☆2,057Updated this week
- A toolkit that streamlines and automates the generation of model cards☆432Updated last year
- Source code/webpage/demos for the What-If Tool☆946Updated 7 months ago
- Interpretability and explainability of data and machine learning models☆1,685Updated 2 months ago
- Algorithms for explaining machine learning models☆2,495Updated last month
- Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.☆553Updated 11 months ago
- OmniXAI: A Library for eXplainable AI☆918Updated 9 months ago
- Bias Auditing & Fair ML Toolkit☆715Updated last month
- Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...☆390Updated 2 years ago
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,401Updated 5 months ago
- A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitig…☆2,577Updated 4 months ago
- MLOps examples☆1,940Updated 9 months ago
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆349Updated last week
- XAI - An eXplainability toolbox for machine learning☆1,168Updated 3 years ago
- Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.☆2,384Updated 4 months ago
- Python library for implementing Responsible AI mitigations.☆66Updated last year
- MLOps using Azure ML Services and Azure DevOps☆1,242Updated last year
- An end-to-end implementation of intent prediction with Metaflow and other cool tools☆859Updated last year
- This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and b…☆1,321Updated 3 months ago
- Official community-driven Azure Machine Learning examples, tested with GitHub Actions.☆1,858Updated this week
- Fit interpretable models. Explain blackbox machine learning.☆6,476Updated 2 weeks ago
- The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process …☆507Updated 4 years ago
- Algorithms for outlier, adversarial and drift detection☆2,365Updated this week
- Repo to hold examples of responsible model assessment for a variety of different verticals such as healthcare and financial services☆64Updated last year
- The Fuzzy Labs guide to the universe of open source MLOps☆461Updated 9 months ago
- The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to s…☆698Updated last month
- 🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞☆720Updated last year
- A collection of research materials on explainable AI/ML☆1,494Updated last month
- Project for open sourcing research efforts on Backward Compatibility in Machine Learning☆73Updated last year