AI Observability
Maintain trust in AI solutions. Ensure consistent AI performance with built-in guardrails, including NVIDIA NeMo for GPU optimization and open-source guard models. Automate real-time monitoring, prompt moderation, blocking and custom alerts to protect reliability and trust.
Unify observability across your AI landscape. Get a unified view of your agents and all their components—tools, prompts, predictive models, LLMs, and more—no matter where they’re built or deployed. Monitor key metrics like LLM cost, toxicity, bias, and vector database performance to eliminate blind spots and maintain control as your AI scales.
Stay flexible with an AI gateway. Avoid rigid architectures and keep your infrastructure adaptable. Easily manage and update execution environments, pipelines, prediction jobs, and resource provisioning. Swap tools in and out based on performance, needs, and changing requirements, without disrupting workflows.
Customize and track what matters most. Gain critical insights with built-in or custom metrics like GPU cost, ROI, or user feedback – from one place. Automate retraining, risk mitigation, alerts, and more with configurable policies tailored to your needs. Use the built-in value tracker for project-level ROI and informed decision-making.
Track, version, and organize use cases. Improve transparency and streamline navigation by organizing AI projects into structured use cases. Automatically document and version all key assets – vector databases, LLMs, AI apps, and notebooks – with built-in role-based access controls for secure collaboration and traceability.
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.
Interpret complex relationships. Visualize complex data structures across generative and predictive use cases, like explaining geospatial models or analyzing vector database coverage.
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
Filter, search, and sort tracing tables for keywords, and blocked prompt patterns
Dive into drift. Identify and investigate drift patterns, including embedding drift for agentic AI and text or location drift for predictive models. Visually compare scoring data segments or scoring vs. training segments across any feature and time window. Access contextual insights like prediction value over time to pinpoint root causes faster.
Maintain high-quality vector databases. Keep your vector databases accurate and aligned. Monitor embedding drift, and track, version, and manage key components – including metadata, benchmarks, and validation results – with tools to quickly surface gaps and update vector databases without disrupting deployments.
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
Retrain and alert for quick resolution. Keep your best models in production with automated retraining and challenger policies. Easily compare your production models with challengers – whether built in DataRobot or externally. Configure real-time alerts so the right teams can act fast and minimize disruptions.
“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.”