Last summer, AI platform DataRobot was struggling. The startup unicorn had laid off a quarter of its employees and appointed a new CEO, former Google and Amazon executive Debanjan Saha, who had served as president and COO since the beginning of 2022.
But today, the company capped a comeback by unveiling its new AI platform 9.0, along with deeper partner integrations, AI accelerators, and redesigned service offerings — which are all focused on helping organizations “derive measurable value from their AI investments.”
The new AI platform includes Workbench, a user experience that supports users with code-first and no-code approaches; reduced enterprise risk through bias mitigation, centralized model monitoring and automated model compliance; and new AI service packages.
Using AI and ML to solve ‘real-life business problems’
“What attracted me to DataRobot is using AI and ML to solve real-life business problems,” said Saha, who called DataRobot the “intelligence layer,” of the data stack, a developing category between the data layer (such as Snowflake, DataBricks, hyperscalers) and application layer (including SAP, Salesforce, and ServiceNow).
The new DataRobot platform, he said, comes as enterprises are at an inflection point. “Everybody’s saying, ‘Okay, I have made a lot of investment in AI, but given the current economic environment, I want to see some real business outcomes.’”
New DataRobot integrations and partnerships
With the latest release, the DataRobot AI Platform Single-Tenant SaaS is now available on AWS, Google Cloud and Microsoft Azure. For on-premises and private cloud customers, DataRobot now supports Red Hat OpenShift for faster installations and deployments that integrate with existing enterprise IT investments.
DataRobot also unveiled several new and deeper partnerships, including an enhanced Snowflake integration for data preparation, model building and monitoring. In addition, it announced a partnership with SAP to help enterprises leverage business data from SAP HANA Cloud and other third-party data sources to build custom ML models in DataRobot and embed them into an SAP application stack.
DataRobot is also integrating the generative AI technology from the Microsoft Azure OpenAI Service to modernize both the code-first notebook experience for experimentation through assisted code generation, and the collaboration experience between the data scientist and business stakeholder.
Built for data scientists by data scientists
“My main focus is to create value for our customers,” said Saha, who emphasized that DataRobot’s end-to-end AI platform is built by data scientists for data scientists. “It’s about fitting into the data and application ecosystem in a seamless way so that people can use AI in their existing environment and leverage the existing investments they have made in that infrastructure.”
Without that, he warned, there is a great deal of friction. “If we create a walled garden, they have to reimplement a lot of things they have already done,” he said.
Ultimately, Saha said it’s all about helping the customer. “We can help them bridge that last-mile gap, from their vision to value,” he said. “That’s what I think will make a huge difference, and that’s the reason I came to DataRobot.”
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