Automated machine learning vendor DotData looks to enter the IoT market with DotData Stream, a new product designed for real-time analytics in low-latency, low-memory environments, such as at the edge.
The product, announced Tuesday, may enable DotData to better compete in manufacturing and finance fields, which typically rely on real-time analytics or heavily use IoT devices.
New use cases
"DotData Stream now opens an entirely new world of IoT use cases for DotData's autoML platform," Forrester analyst Mike Gualtieri said.
While it's priced as a separate product, DotData Stream pairs with DotData Enterprise, the company's flagship automated machine learning platform. Customers can build a machine learning model within DotData Enterprise, then export it as a Docker image with an embedded model to provide predictive capabilities separate from DotData Enterprise.
Customers can then deploy the low-latency, low-memory model on IoT devices, and use it for use cases that require real-time processing, such as fraud detection, credit approval or predictive maintenance.
DotData Stream now opens an entirely new world of IoT use cases for DotData's autoML platform.
Mike GualtieriAnalyst, Forrester
"DotData Stream is ideal for applications where real-time prediction services are needed," said Ryohei Fujimaki, CEO and founder of DotData.
"A few common use cases for DotData Stream are instant credit approval, fraud detection, automated underwriting, dynamic pricing and industrial IoT," he said.
Competing automated machine learning
Automated machine learning, the process of automating parts of machine learning development, is a relatively new field. DotData, founded in 2018, competes against a handful of vendors in the field, including leaders DataRobot and H20.ai.
Both DataRobot and H20.ai enable edge model deployments, Gualtieri said. DotData Stream allows DotData to now compete in industrial IoT application on the edge.
"The use of autoML has the potential to be a game changer for manufacturing companies because autoML is significantly faster way to develop models," Gualtieri said.
Automated machine learning vendor DotData has released DotData Stream for IoT applications.
However, he added, "AutoML is not yet widely used for IoT use cases, even though the potential exists."
"I don't think any one of these vendors dominates in edge deployments at this time," Gualtieri said.
While DotData Stream does open new markets for the vendor, the company may still need to prove to potential enterprise customers in the finance and manufacturing industries that it has the chops to tackle certain use cases.
"Enterprise buyers expect to see domain expertise in understanding specific use cases, such as fraud detection and predictive maintenance," Gualtieri said.
