AI Observability
Maintain trust in generative AI. Ensure the performance of your generative AI models by combining DataRobot’s library of guardrails and open-source guard models. Automate real-time monitoring and intervention strategies like blocking prompts and custom alerts.
Detect and address PII leakage, prompt injection, hallucinations and other threats
Integrate with a wide variety of frameworks from Nvidia, Microsoft, and more
Bias and fairness checks
Moderate both prompts and responses
Track what matters most. Get clear insights into custom-built metrics – like cost, ROI, or response correctness – from one place. Calculate custom metrics within DataRobot or effortlessly track changes over time externally.
Compute your own custom metrics within DataRobot or bring external metrics for monitoring
Ensure business continuity. Gain full visibility into model performance, whether built in DataRobot or externally. Track shifts in drift, accuracy, service health, and data quality, and quickly perform root-cause analysis to resolve issues efficiently.
Challenge and retrain your models. Keep your best models in production. Automate retraining and challenger policies to quickly compare your production models with challengers—whether built in DataRobot or externally. Use built-in metrics to align comparisons with your business goals and generate insights for deep, intuitive performance analysis.
Automate retraining policies and challenger comparisons to maintain performance.
Track model versions and hierarchy. Ensure transparency and compliance in your AI pipelines with automated documentation and versioning. Quickly generate detailed reports and access audit trails so you can maintain efficiency and meet regulatory requirements effortlessly.
Automated documentation to capture complete history, versioning, and workflows.
Capture end-to-end lineage. Track the full lifecycle of your AI strategies from experimentation to production. DataRobot packages everything—data transformations, feature discovery, LLMs, vector databases, and prompting strategies—making it simple to audit and track every deployment end-to-end.
Trace pipeline lineage from deployment to data preparation.
Interpret complex relationships. Enhance interpretability and visualize complex data structures across generative and predictive use cases, like explaining geospatial models or analyzing vector database coverage.
UMAP insights to visualize distance between documentation and prompts
Visualize SHAP-over-time
Two-way PDP and ICE plots
Analyze geospatial models
Trace and assess prompts. Gain prompt and response insights with automatically generated tracing tables to improve your vector databases and generative AI responses. Monitor production prompts for response quality, user feedback, PII flags, rouge, or custom metrics to better manage risk.
Monitor prompts and responses in production
Custom metrics to uncover negative feedback, documentation gaps,and areas of improvement
Generate tracing tables for vector databases created in DataRobot or externally
Filter, search, and review tracing tables for keywords, and blocked prompt patterns
Dive into drift. Uncover deep insights into drift patterns by visually comparing scoring data segments or scoring and training segments across any feature and time period. Access contextual details like prediction value over time to support your investigation and identify root causes.
Compare drift heat maps across features to identify correlated trends
Visualize drift and predictions over time
Track and visualize drift for text-based features
Schedule drift status monitoring and configure notifications
Maintain high-quality vector databases. Keep your vector databases relevant and comprehensive by tracking, versioning, and managing all aspects, including metadata, benchmarks, and validation results. Quickly identify areas of improvement and gain deep insights into deployments for continuous optimization.
Track vector database versions and documents
Manage vector databases built in DataRobot or bring your own
Visual vector database insights for quality assessment
Automatically update deployments with new vector database version
Collaborate and alert for quick resolution. Improve collaboration with customizable notifications that alert the right teams if performance degrades. Real-time alerts ensure your organization can quickly respond to changes in deployments and minimize disruptions.
Configure event triggers and user notifications
“With open-source software, optimizing a model could take anywhere from two weeks to a month. And we needed to be able to explain the dynamics underlying the model build and its predictions.”
“DataRobot’s rich machine learning blueprints, feature engineering methods and explainability features amongst others make it a cornerstone in BMW Group’s AI Platform to scale AI adoption.”