DataRobot Announces New Enterprise-Grade Functionality to Close the Generative AI Confidence Gap and Accelerate Adoption

Large language model cost and performance monitoring, observability console, multi-provider comparison playground and other enhancements unveiled to deliver greater transparency and governance

DataRobot, the leader in Value-Driven AI, today announced new end-to-end functionality designed to close the generative AI confidence gap, accelerating AI solutions from prototype to production and driving real-world value. Enhancements to the DataRobot AI Platform empower organizations to operate with correctness and control, govern with full transparency, and build with speed and optionality.

“The demands around generative AI are broad, complex and evolving in real-time,” said Venky Veeraraghavan, Chief Product Officer, DataRobot. “With over 500 of our customers deploying and managing AI in production, we understand what it takes to build, govern, and operate your AI safely and at scale. With this latest launch, we’ve designed a suite of production-ready capabilities to address the challenges unique to generative AI and instill the confidence required to bring transformative solutions into practice.”

New functionality announced today brings enterprise-grade governance and observability for both generative and predictive AI in the DataRobot AI Platform:

– 360° Observability Console: New full-360° view observability for DataRobot and third-party models across cloud providers, on-premises, or at the edge. The console acts as a command and control center with comprehensive insights into the performance, behavior, and health of AI systems and models, allowing for immediate detection of issues or anomalies.

– LLM Cost and Performance Monitoring: Out-of-the-box monitoring to observe and manage your generative AI solutions and costs in real-time with customizable metrics designed for both high performance and on-target budgets. Now you can see cost per prediction and total spend by generative AI solution, set your own alerts to avoid exceeding thresholds, and make cost-performance tradeoff decisions.

– Generative AI Guard Models: Assess critical attributes like toxicity, sentiment, PII leaks, prompt injection, and correctness for generative AI models in production, as well as capture user feedback on the quality of generated responses to improve models over time.

– Unified AI Registry: A single system of record to govern all of your generative and predictive AI assets, including models from different systems. The new centralized Registry helps you track what matters while unifying disparate projects, processes, and multi-cloud environments for seamless cross-team visibility and collaboration across your organization.

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– Multi-Provider LLM Playground: First-of-its-kind visual interface with built-in access to GCP Vertex AI, Azure OpenAI and AWS BedRock to easily compare and experiment different recipes with any combination of foundation model, vector databases, and prompting strategies.

– Generative AI Accelerators: New accelerators designed to quickly accomplish common tasks for accelerating generative AI deployment, such as building and hosting a Retrieval Augmented Generation (RAG) application, adding custom metrics, monitoring external models, and integrating with business applications like Slack and Microsoft Teams.

“DataRobot has been instrumental as we work through our generative and predictive AI use cases,” said Fred De Letter, Senior Director of Business Insights and Analytics, Keller Williams. “With DataRobot’s LLM operations (LLMOps) capabilities and out-of-the-box LLM performance monitoring, we’re equipped to implement cutting-edge generative AI techniques into our business while monitoring for toxicity, truthfulness and cost.”

“AI is set to shape our future in many different ways, and it has enormous potential to be a powerful force for good,” said Craig Civil, Director of Data Science and Artificial Intelligence, The British Standards Institution (BSI). “In BSI’s research, we have seen evidence of a confidence gap, with people lacking understanding and trust in AI. Ensuring confidence in AI systems is key to society realizing AI’s transformational benefits, which is why we’ve partnered with DataRobot for our generative AI journey.”

“DataRobot has long been recognized for simplifying the complexities presented by AI and data science. Our research shows that organizations need flexibility to use their preferred LLMs and infrastructure through an open, tightly-integrated ecosystem,” said Mike Leone Principal Analyst at Enterprise Strategy Group. “By bringing together predictive and generative AI in a single end-to-end AI lifecycle platform with governance and cost transparency, DataRobot is delivering real value to its customers as they look to scale AI projects.”

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