ScaleOut Software Adds Machine Learning Capabilities to its Twin Streaming Service

ScaleOut Software is adding major extensions to its ScaleOut Digital Twin Streaming Service that enable real-time digital twin software to implement and host machine learning and statistical analysis algorithms.

Real-time digital twins can now make extensive use of Microsoft’s ML.NET machine learning library to implement these groundbreaking capabilities for virtually any IoT device or source object.

Integration of machine learning with real-time digital twins offers powerful new options for real-time monitoring across a wide variety of applications, according to the vendor.

For example, cloud-based real-time digital twins can track a fleet of trucks to identify subtle changes in key engine parameters with predictive analytics that avoid costly failures. Security monitors tracking perimeter entrances and sound sensors can use machine learning techniques to automatically identify unexpected behaviors and generate alerts.

By harnessing the no-code ScaleOut Model Development Tool, a real-time digital twin can easily be enhanced to automatically analyze 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 analyzing numeric parameters contained within incoming messages to spot issues as they arise:

  • Spike Detection: Tracks a single parameter from a data source to identify a spike in its values over time using an adaptive kernel density estimation algorithm implemented by ML.NET.
  • Trend Detection: Also tracks a single parameter to identify a trend change, such as an unexpected increase over time for a parameter that is normally stable, using a linear regression algorithm that detects inflection points.
  • Multi-Variable Anomaly Detection: Tracks a set of related parameters in aggregate to identify anomalies using a user-selected machine-learning algorithm implemented by ML.NET that performs binary classification with supervised learning.

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.”

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