- New features empower users to easily build powerful digital twin applications for real-time monitoring and simulation
- This technology can be applied across numerous industries, including transportation networks, security systems, smart cities, and military asset tracking.
ScaleOut Software has introduced generative artificial intelligence (GenAI) and machine-learning (ML) powered enhancements to its ScaleOut Digital Twins cloud service and on-premises hosting platform with the release of Version 4.
According to company officials, this latest release introduces GenAI integration through OpenAI’s large language model, significantly expanding the ability of digital twins to analyze data, detect anomalies and provide real-time insights when monitoring complex live systems. By leveraging these capabilities, operations managers can quickly pinpoint and address emerging issues while reducing their workload.
Additionally, the update adds automatic retraining for ML algorithms running within digital twins, continuously improving their monitoring capabilities as they process new telemetry data.
Harnessing AI and ML
Dr. William Bain, CEO and founder of ScaleOut Software stated that the ScaleOut Digital Twins Version 4 marks a pivotal step in harnessing AI and ML for real-time operational intelligence.
“By integrating these technologies, we’re transforming how organizations monitor and respond to complex system dynamics — making it faster and easier to uncover insights that would otherwise go unnoticed,” said Bain in a statement. “This release is about more than just new features; it’s about redefining what’s possible in large-scale, real-time monitoring and predictive modeling.”
New Release Benefits
The release features move real-time monitoring towards fully autonomous operations that boost both safety and efficiency in managing large, complex systems. This technology can be applied across numerous industries, including transportation networks, security systems, smart cities, and military asset tracking.
Key features of this revised ScaleOut Digital Twins includes:
- Enables users to leverage generative AI for continuous, real-time anomaly detection. This feature automates the real-time monitoring process, enabling faster detection of emerging issues while freeing operations managers from constant dashboard surveillance.
- Easily create data visualizations and queries with natural language prompts. This streamlines operations managers’ workflows by allowing AI to assist in creating insightful visualizations and queries quickly and efficiently.
- Automatically Retrain ML algorithms in live systems. By working together, digital twins can generate and use real-time data to retrain ML algorithms without interrupting operations. Additionally, users also have the option to access this retraining data for manual retraining and redeployment
- Additional Collaboration and Performance Enhancements, incorporating ML algorithms from TensorFlow in addition to Microsoft ML.NET, offering users more options for deploying machine learning models. Digital twins can also now quickly access and share data using an in-memory data grid.
Scalable Software
Founded in 2003, the Washington state-based ScaleOut Software develops leading-edge software that delivers scalable, highly available, in-memory computing and streaming analytics technologies to a wide range of industries. With Version 4, application developers can take advantage of ScaleOut’s open-source APIs to construct digital twin models for real-time monitoring and simulation on the ScaleOut Digital Twins platform.
To streamline development, an open-source workbench allows developers to test applications before deploying them across thousands of digital twins.
The platform supports the live analysis of data from IoT devices and other sources, delivering actionable insights in seconds. It also runs large-scale simulations to optimize the design and operation of complex systems such as transportation networks, logistics operations, military scenarios, and smart cities.