Media Coverage: ScaleOut Adds Machine Learning to its Digital Twin Service

ScaleOut Software has released major extensions to its Digital Twin Streaming Service that enable real-time digital twin software to implement and host machine learning (ML) and statistical analysis algorithms that immediately identify unexpected behaviors in incoming telemetry. Real-time digital twins can now make extensive use of Microsoft’s ML.NET machine learning library to implement these capabilities for virtually any IoT device or source object.

By harnessing the no-code ScaleOut Model Development Tool, a real-time digital twin can easily be enhanced to automatically analyse incoming telemetry messages using machine learning techniques. Machine learning provides important real-time insights that enhance situational awareness and enable fast, effective responses. The tool provides three configuration options for analysing numeric parameters contained within incoming messages to spot issues as they arise.

Once configured through the ScaleOut Model Development Tool, the ML algorithms run automatically and independently for each data source within their corresponding real-time digital twins as incoming messages are received. Each real-time digital twin can automatically capture anomalous events for follow-up analysis and generate alerts to popular alerting providers, such as Splunk, Slack, and Pager Duty, to support remediation by service or security teams.

“We are excited to offer powerful machine learning capabilities for real-time digital twins that will make it even easier to immediately spot issues or identify opportunities across a large population of data sources,” said Dr. William Bain, ScaleOut Software’s CEO and founder. “ScaleOut Software has built the next step in the evolution of the Microsoft Azure IoT and ML.NET ecosystem, and we look forward to helping our customers harness these technologies to enhance their real-time monitoring and streaming analytics.”