Boston-headquartered AI platform DataRobot has 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, in data center and edge areas and is now available to all businesses in a multi-cloud architecture.
“Businesses are now navigating unspecified market challenges – from the long -term impact of a prolonged pandemic, to unreliable supply chains, to a rapidly approaching return to work,” he said. Nenshad Bardoliwalla, Chief Product Officer of DataRobot. “AI has the potential to help every business manage in this unprecedented era. But your AI platform must be able to anticipate and adapt faster and smarter to even the most unpredictable conditions. With DataRobot AI Cloud 8.0, we empower businesses to better anticipate moments of change and continue to optimize machine learning models, even those already in production, while driving new and more accurate decisions up to the front line business users. “
According to Mckinsey’s State of AI 2021 report, 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.
New features of DataRobot AI Cloud 8.O include:
No-code AI app builder
The platform added Time Series capabilities to the AI App Builder. The Automated Time Series allows you to create smart, AI-driven using advanced algorithms, automation, and time-aware guardrails. Within the app, you can compare predictions to actual values for new data, provide insights into prediction explanations over time, and dig deeper into the reasons why each prediction is driving.
Continuous AI
Continuous AI is now available for on-prem users. Continuous AI combines the best in automated machine learning with the best machine learning operations to continuously improve models throughout their lifecycle. With Continuous AI, you create multiple MLOps Retraining Strategies to refresh your production models based on the schedule you choose — such as when accuracy drops below a predefined threshold, or data drift occurs. , or when models fail to keep up with the essential business skills that reinforce. trust, ethics, and anti-bias. Not only does fluid AI train your current production models for you, it also develops and tests a whole host of new models and presents leaders as recommended challengers as part of the same process. Challengers will be replayed against historical prediction data for you (or the system) to decide if one of them should be promoted as the new champion.
Photo: DataRobot
Active directory connections for SQL server, Synapse
Added Active Directory Connect platform to Azure Synapse. The connector lets you connect to Azure Synapse Analytics for Library imports and exports. For export, the connector uploads data to Azure’s Data Lake service and then exposes the data as a table to the 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 build complete and highest quality models.
Scoring code for Snowflake
DataRobot Scoring Code supports execution directly within Snowflake, using Snowflake’s new Java UDF functionality. This eliminates the need to extract and load data from Snowflake.
Recently, DataRobot appointed Debanjan Saha as the new President and Chief Operating Officer (COO) of the company. Last year in July, the company acquired the machine learning operations (MLOps) platform, Algorithmia. In May, the company announced the acquisition of cloud data science and analytics platform Zepl.g
Last year, it announced 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.