Easily deploy & manage your machine learning deployments while ensuring transparency and compliance.
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Deploy, Update & Oversee
- Deploy models in a few steps along with explainers & transformers.
- Access pre-deployment compliance best practices insights
- Easily deploy new model versions or change deployment details
- Maintain oversight of all running models & their main metrics
Explain Predictions & Collect Feedback
- Easily deploy standard, trained & integrated explainers
- Traceback & visualize explanations in-app
- Collect evaluations of predictions from experts or end-users
- Monitor disagreement ratio & continuously improve model performance
Monitor Model Performance
- Monitor traffic for activity, errors & response time
- Use actuals to monitor performance with metrics for classification, regression, and text generation models
- Monitor model drift
- Create and manage alerts
Ensure Compliance with Regulations & Policies
- Traceback all changes, predictions & explanations within a deployment
- Clear ownership, documentation & model cards
- Checklists & documentation on latest regulations & best practices
- Upload your own checklists & documentation
- Assign ownership of models
Seamlessly integrates into your current workflow
- Support for major AI frameworks: pytorch, sklearn, tensor flow, xgboost,…
- Support for major XAI frameworks: shap, anchors, MACE, PDP
- Integrations with Sagemaker, Azure ML, MLFlow, HuggingFace,…
- Intuitive UI, Python SDK, and extensive API layer
- Available on the AWS and Azure Marketplace within your commit
