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DataRobot, the Boston-based AI platform, released AI Cloud 8.0 to help organizations drive growth, reduce operational costs and improve customer engagement. DataRobot AI Cloud 8.0 can be deployed in public clouds, on-premises in the data center and at the edge and is available today for all enterprises in a multi-cloud architecture.

“Businesses today face uncharted market challenges – from the lasting impact of the prolonged pandemic, to unreliable supply chains, to a rapidly approaching return to work,” Nenshad said. Bardoliwalla, Chief Product Officer at DataRobot. “AI has the potential to help every business through this unprecedented time. But your AI platform must be able to anticipate and adapt faster and smarter to the most unpredictable market conditions. With DataRobot AI Cloud 8.0, we empower companies to better anticipate times of change and continuously optimize machine learning models, even those already in production, while empowering frontline business users to take on new more precise decisions.

According to 2021 State of AI Report by MckinseyAI adoption is on the rise: 56% of all respondents said they have adopted AI in at least one function, up from 50% in 2020.

New features of DataRobot AI Cloud 8.O include:

No-code AI app builder

The platform has added Time Series functionality to AI App Builder. Automated Time Series lets you create robust AI-powered forecasts using advanced algorithms, automation, and time-sensitive guardrails. In the app, you can compare predictions with actual values ​​of new data, provide insights into prediction explanations over time, and dig deeper into the reasons behind each prediction.

AI continues

Continuous AI is now available for on-premises users. Continuous AI combines the best of automated machine learning with the best of machine learning operations to continuously improve models throughout their lifecycle. With Continuous AI, you create multiple MLOps recycling strategies to refresh your production models based on a schedule of your choosing, such as when accuracy drops below a predetermined threshold, or data drift occurs, or when models fail to keep pace with essential business practices that build trust, ethics and anti-bias. Continuous AI not only recycles your current production models for you, it also generates and tests a whole host of new models and presents the best ones as recommended challengers as part of the same process. Challengers are then replayed against historical prediction data for you (or the system) to decide if one should be promoted as the new champion.

Image: DataRobot

Active Directory Connections for SQL Server, Synapse

The platform added Active Directory Connect with Azure Synapse. The connector lets you connect to Azure Synapse Analytics for library imports and exports. For export, the connector loads the data into Azure’s Data Lake service and then exposes the data as a table in SQL Data Warehouse.

Users will have access to a wide range of data sources such as AWS Redshift, Oracle, SAP Hana and Google BigQuery, giving them the power to create comprehensive models of the highest quality.

Scoring code for Snowflake

The DataRobot notation code supports running directly in Snowflake, using Snowflake’s new Java UDF feature. This removes the need to fetch and load data from Snowflake.

Recently, DataRobot appointed Debanjan Saha as the company’s President and Chief Operating Officer (COO). Last year, in July, the company acquired the Machine Learning Operations Platform (MLOps), algorithm. In May, the company announced the acquisition of cloud-based data science and analytics platform Zepl.g

Last year, it announced a $300 million Series G investment led by Altimeter Capital and Tiger Global along with new investors Counterpoint Global (Morgan Stanley), Franklin Templeton, ServiceNow Ventures and Sutter Hill Ventures.


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