We just launched Cloudexplain, a platform that makes Explainable AI (XAI) effortless for ML Models – with just few lines of code. As data scientists ourselves, we were frustrated by how much time gets wasted trying to explain ML Models – visualizing SHAP values, creating dashboards, understanding black-box models, or manually building reports for stakeholders. We built Cloudexplain to automate this and solve the current shortcomings of open-source XAI libraries.

What it does: - generates standardized interactive XAI visualizations with just a few lines of code - support for open-source ML libraries (supports scikit-learn, XGBoost, TensorFlow, PyTorch, etc.) - model explanations, feature importance - scales to millions of predictions via cloud infrastructre scaling (only on hosted plan) - let's you run XAI procedures in parallel to inference runs - collaboration-ready insights for both devs and non-tech stakeholders - easy integration via Python SDK or REST API - helps fulfill GDPR or CCPA requirements

Check it out, have a look at the sample use case and let us know what you think.