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DataRobot today announced DataRobot Core, an offering that broadens the company’s AI development tool portfolio with new resources, functionality, and community features. The launch of Core coincides with the release of the latest version of DataRobot’s enterprise platform, which the company says introduces “broader and more sophisticated” analytical capabilities as well as new ways to manage AI systems in production.

The demand for AI, combined with the complexity in achieving deployment, has created delays in data science initiatives for businesses. While Harvard Business Review reports that 52% of companies accelerated their AI adoption plans because of the COVID-19 crisis, many expect that challenges will impede progress. According to a recent Teradata survey, businesses see a lack of IT infrastructure and access to talent as major blockers, followed by lack of budget for implementation, complications around policies, regulations and rights, and impact on customer expectations.

DataRobot Core is designed to help enterprises overcome these hurdles by giving data scientists more flexibility in how they deliver AI to the organization, ostensibly enabling faster time to value, DataRobot chief product officer Nenshad Bardoliwalla says. “For organizations today, translating data and AI into tangible outcomes is critical in order to remain competitive and thrive,” he added in a statement. “DataRobot Core [meets] increasing demand and scale, and empower[s] 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.”

DataRobot and platform version 7.3

DataRobot’s technology runs on cloud platforms, on-premises datacenters, or as a fully managed development service. Once it’s deployed, customers can use it to monitor machine learning models from a dashboard and test, run, and maintain the models to optimize outcomes.

DataRobot Core, like DataRobot’s enterprise AI platform, provides a unified environment with a programming notebook; tools for “code-first” as well as automated model generation; pipelines on top of Apache Spark, the open source analytics engine; and an API to enable programmatic access to DataRobot’s full platform. It also comes with a collection of accelerators, third-party integrations, and libraries to expedite AI delivery and drive efficiency, along with educational resources and knowledge curated by DataRobot team members and customers.

In support of DataRobot Core, DataRobot is establishing an expanded partnership with AtScale, a data virtualization company, to deliver improved feature modeling to customers. (“Feature modeling” in machine learning refers to the process of selecting variables when creating a model.) Bardoliwalla says that AtScale can simplify connections from DataRobot to a range of cloud data platforms while providing a “modeling canvas” for feature engineering.

Arriving alongside DataRobot Core, DataRobot version 7.3 introduces over 80 new features and capabilities, according to Bardoliwalla — including anomaly detection with images. Automated compliance documentation now extends to custom models built outside of DataRobot, moreover, meaning users can evaluate and compare “challenger” models against live models to see if a model should be replaced in order to maintain a threshold level of accuracy.

“[W]hile more companies and business leaders are prioritizing AI, we still see teams struggle with long deployment timelines,” Bardoliwalla added. “With DataRobot Core, data scientists now have a complete portfolio of code-first capabilities that give them ultimate flexibility in how they deliver AI to the business … For the IT decision-maker, both DataRobot Core and DataRobot 7.3 are significant because they help put AI into production faster and enable data scientists with tools that alleviate the common challenges of AI, saving them time and enabling them to focus on more complex business challenges.”

Growth in data science platforms

While deploying AI can be fraught with challenges, companies that successfully overcome the issues report reaping significant advantages. In a study by S&P Global’s 451 Research division, the top three benefits enterprises realize from investing in AI include lowering costs, increasing sales, and improving workforce productivity.

Improving business agility is another widely expected plus. A separate report from Statista found that AI has delivered returns on investments as expected — or more than expected — for around 1,000 professionals across various industries.

DataRobot competes with a number of enterprise AI development platforms, but it has found early success, raising hundreds of millions of dollars in venture capital at a multi-billion-dollar valuation. Founded by Jeremy Achin and Tom de Godoy, the company is one of the top-funded AI startups in the world, and it has used its war chest to purchase startups including Algorithmia, Nutonian, Nexosis, Paxata, Cursor, and ParallelM.

DataRobot claims to have had triple-digit recurring revenue growth dating back to 2015, as well as more than 2 billion models built on the platform to date. The company’s customers span more than a third of the Fortune 50, including Kroger, Nationwide, Lenovo, PNC, and others across banking, health care, insurance, finance, manufacturing, retail, government, sports, and gaming verticals.

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