DataRobot Core helps data scientists deliver more sophisticated machine learning models faster – SiliconANGLE News

DataRobot Inc. is updating its suite of artificial intelligence tools with a new offering called DataRobot Core that’s aimed at data science experts.

The ecompany also announced a new version of its AI Cloud platform that adds more sophisticated analytical capabilities and enhanced decision intelligence tools.

DataRobot sells tools that reduce the amount of time and custom code necessary to create machine learning software. Its main product is DataRobot AI Cloud, a point-and-click AI builder that makes it possible to create a machine learning model without any coding skills.

Announced today, DataRobot Core is said to bring together a complete portfolio of purpose-built capabilities for data scientists that allow for greater flexibility in how they deliver AI software. The company is promising faster experimentation and time to value, plus clearer business impact from AI.

DataRobot Core is, in essence, a unified environment for building machine learning models that enables what the company calls “Composable ML.” Data science teams can quickly pivot between code-first and automated machine learning model generation. The platform includes features such as code-centric pipelines on top of Apache Spark and an open application programming interface that provides programmatic access to DataRobot’s flagship AI Cloud platform.

Besides the actual platform, DataRobot Core also comes with an extensive portfolio of accelerators, third-party integrations and libraries that together help expedite AI delivery. In addition, users will benefit from community support, with access to shared knowledge, the expertise of DataRobot’s own data scientists, as well as “thousands of community members” from the company’s customers.

Meanwhile, the launch of DataRobot AI Cloud 7.3 adds more than 80 new features and capabilities and promises to enable AI-driven decisions across all lines of business, the company said.

Among the new features are extended support for more diverse use cases through greater flexibility to use more data types to build AI models. For instance, users can now run anomaly detection with images, leverage an improved version of Text AI and access new tools for multimodal clustering, time-series segmented modeling and multilabel classification.

The update also adds decision intelligence capabilities, meaning it’s now possible to deploy machine learning models with DataRobot that combine complex rules and business logic with post-process prediction scores in a simple API. Through that, the company explains, it should be possible to build fully customized AI apps atop a time series model with minimal expertise.

DataRobot Chief Product Officer Nenshad Bardoliwalla said it’s critical for organizations to be able to translate data and AI into tangible business outcomes.

“DataRobot Core and 7.3 are designed to meet increasing demand and scale, and empower the largest number of AI creators, from code-centric data science teams to business analysts and decision makers, to experiment fast and collaborate effectively on the same platform,” he said.

Holger Mueller, an analyst with Constellation Research Inc., told SiliconANGLE that while building AI automation is hard, deploying it and keeping it going is exponentially more difficult. Add to that, many of those operational aspects must be handled by qualified data scientists, who’re in high-demand and command big salaries. So it’s not easy to see how AI can be prohibitively expensive for many organizations.

“The solution is pre-built AI platforms like DataRobotc, which automates many steps of the AI app life cycle,” Mueller said. “The price is a dependency on the platform however, so companies will need to weigh up the tradeoff between higher efficiency and dependency, versus lower efficiency and more flexibility.”

Image: DataRobot

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