Boston-headquartered AI platform DataRobot has released AI Cloud 8.0 to help organisations drive growth, reduce operational costs, and improve customer engagement. DataRobot AI Cloud 8.0 can be deployed to public clouds, on premises in the data centre and at the edge and is available today to all businesses in a multi-cloud architecture.
“Businesses today are navigating uncharted market challenges – from the lasting impact of the prolonged pandemic, to unreliable supply chains, to a rapidly approaching return to work,” said Nenshad Bardoliwalla, Chief Product Officer at DataRobot. “AI has the potential to help every business manage through this unprecedented time. But your AI platform must be able to anticipate and adapt faster and more intelligently to even the most unpredictable market conditions. With DataRobot AI Cloud 8.0, we’re empowering businesses to better anticipate moments of change and continuously optimise machine learning models, even those already in production, while driving new and more accurate decisions down to front line business users.”
As per the State of AI report 2021 by Mckinsey, AI adoption is on the rise: 56 percent of all respondents reported AI adoption in at least one function, up from 50 percent in 2020.
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The new features of the DataRobot AI Cloud 8.O include:
No-code AI app builder
The platform has added the Time Series capabilities to the AI App Builder. The Automated Time Series allows you to create robust, AI-driven forecasts using advanced algorithms, automation, and time-aware guardrails. Within the app, you can compare forecasts with actual values for new data, provide insights on prediction explanations over time, and dig deeper into the reasons driving each forecast.
Continuous AI is now available for on-prem users. Continuous AI combines the best of automated machine learning with the best of machine learning operations to continually improve models over their full lifecycle. With Continuous AI, you create multiple MLOps Retraining Strategies to refresh your production models based on the schedule of your choosing—like when accuracy drops below a predetermined threshold, or data drift occurs, or when models fail to keep pace with essential business practices that reinforce trust, ethics, and anti-bias. Continuous AI not only retrains your current production models for you, it also generates and tests a whole host of new models and presents the top 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 of them should be promoted as the new champion.
Active directory connections for SQL server, Synapse
The platform has 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 uploads data into Azure’s Data Lake service and then exposes the data as a table in the SQL Data Warehouse.
The users will get access to a wide range of data sources such as AWS Redshift, Oracle, SAP Hana, and Google BigQuery giving them the power to build complete, highest quality models.
Scoring code for Snowflake
The DataRobot Scoring Code supports execution directly inside of Snowflake, using Snowflake’s new Java UDF functionality. This removes the need to extract and load data from Snowflake.
Recently, DataRobot appointed Debanjan Saha as the company’s new President and Chief Operating Officer (COO). Last year in July, the company had acquired machine learning operations (MLOps) platform, Algorithmia. In May, the company had announced the acquisition of cloud data science and analytics platform Zepl.g
Last year, it had announced a USD 300 million in Series G led by Altimeter Capital and Tiger Global along with new investors Counterpoint Global (Morgan Stanley), Franklin Templeton, ServiceNow Ventures, and Sutter Hill Ventures.