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.
☆1,389Updated 3 months ago
Related projects ⓘ
Alternatives and complementary repositories for responsible-ai-toolbox
- A Python package to assess and improve fairness of machine learning models.☆1,948Updated this week
- Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world d…☆421Updated 5 months ago
- Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.☆516Updated 5 months ago
- Source code/webpage/demos for the What-If Tool☆918Updated 2 months ago
- Bias Auditing & Fair ML Toolkit☆694Updated 2 months ago
- Algorithms for explaining machine learning models☆2,414Updated this week
- A toolkit that streamlines and automates the generation of model cards☆426Updated last year
- A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.☆3,926Updated this week
- Generate Diverse Counterfactual Explanations for any machine learning model.☆1,365Updated 7 months ago
- nannyml: post-deployment data science in python☆1,979Updated 2 weeks ago
- This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and b…☆1,269Updated this week
- MLOps examples☆1,842Updated 3 months ago
- Tensorflow's Fairness Evaluation and Visualization Toolkit☆342Updated this week
- ♾️ CML - Continuous Machine Learning | CI/CD for ML☆4,039Updated this week
- MLOps using Azure ML Services and Azure DevOps☆1,204Updated last year
- Curated list of open source tooling for data-centric AI on unstructured data.☆701Updated last year
- OmniXAI: A Library for eXplainable AI☆876Updated 3 months ago
- Algorithms for outlier, adversarial and drift detection☆2,249Updated this week
- Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).☆1,400Updated 2 weeks ago
- Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...☆383Updated 2 years ago
- Interpretability and explainability of data and machine learning models☆1,633Updated 4 months ago
- 📖 DVC website and documentation☆338Updated this week
- Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML va…☆3,628Updated this week
- The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process …☆489Updated 3 years ago
- Project for open sourcing research efforts on Backward Compatibility in Machine Learning☆71Updated last year
- Resources for Data Centric AI☆1,101Updated 11 months ago
- Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.☆2,312Updated 4 months ago
- ZenML 🙏: The bridge between ML and Ops. https://zenml.io.☆4,073Updated this week
- A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitig…☆2,462Updated 4 months ago
- A curated list of awesome MLOps tools☆4,125Updated last month