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

(AI generated/Shutterstock)

DataRobot, one of the leading providers of AI solutions for enterprises, has announced new updates to close the generative AI (GenAI) confidence gap and to accelerate AI solutions from prototype to production. The updates include LLM cost and performance monitoring, multi-provider comparison playground, and other upgrades for improved governance and transparency. 

As a value-driven AI company, DataRobot has been on a mission to democratize machine learning and enable AI-driven enterprises. The latest updates in functionality to its platform will help advance the objectives of DataRobot. The new updates not only provide organizations with greater control and transparency, it also help them with speed and optionality. 

“The demands around generative AI are broad, complex, and evolving in real-time,” said Venky Veeraraghavan, Chief Product Officer, at 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.”

Earlier this year, DataRobot released the 9.0 update and new partner integrations to drive AI ROI. The latest updates to the platform include a  360° Observability Console that provides a central command center to monitor and control the performance, behavior, and health of AI models and systems. It allows for immediate detection of issues or anomalies. This feature is available across cloud providers, on-premises, and at the edge. 

The new multi-provider LLM playground includes a first-of-its-kind visual interface with built-in GCP Vertex AI, AWS Bedrock, and Azure OpenAI to compare and experiment with different recipes for a combination of vector databases, foundations models, and prompting strategies. 

The new functionality includes GenAI accelerators that can expedite GenAI deployment tasks such as building and hosting RAG applications, monitoring external models, and adding custom metrics. The GenAI accelerators can also integrate with business applications such as Microsoft Teams and Slack. 

(Paper piper/Shutterstock)

The LLM cost and performance monitoring improvements will help in managing GenAI solutions. It provides greater control and visibility of costs in real-time with customizable metrics. Users can monitor costs per prediction and total spending by GenAI solution. In addition, users can set alerts to avoid exceeding thresholds and make cost-performance tradeoffs.  

Another key upgrade to the platform is a unified AI registry to govern all generative and predictive AI assets. This enhancement helps unify multi-cloud environments, disparate projects, and processes to boost cross-team collaboration and visibility. 

To allow for continuous improvement of the AI models, DataRobot now allows access to critical attributes, such as prompt injection and sentiment, for GenAI in production. In addition, you can now capture user feedback on the quality of GenAI responses to improve models over time. 

“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, at 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.” 

Related Items

 DataRobot Notebooks for Data Science and the Enterprise Now Available

IBM Accelerates Enterprise AI for Clients with New Capabilities on IBM Z

DataRobot CEO Sees Success at Junction of Gen AI and ‘Classical AI’