Twin Sight ML Workbench
Twin Sight ML Workbench empowers industrial teams to upload, run, and visualize machine learning models directly on live operational data and enterprise data —no Python skills required.
Operational ML is Broken. We Fixed It.
Machine learning in industrial environments is often out of reach. Data scientists don’t have access to real operational data. Operators don’t have tools to test models. And the infrastructure needed to bridge that gap is usually slow, complex, and expensive.
The result?
Promising AI initiatives get stuck in “POC purgatory,” operational insights stay siloed, and predictive maintenance and optimization efforts fall flat.
Turn Real-Time Data into Real-World ML Results
Twin Sight ML Workbench is a visual, no-code environment built for industrial users who want to test and apply ML models directly on their live operational data—like AVEVA PI System data—without waiting on IT or learning new tools. Similarly data from Databricks, Snowflake and other IT datalakes can also be streamed, and tested with the same ML models – bridging the OT/IT divide.
Whether you’re a data scientist wanting to run inference on real-time OT data or an engineer testing models to reduce downtime, ML Workbench connects you to results faster.
What It Does:
- Upload pre-trained ML models (ONNX format)
- Stream data from historians and SCADA systems
- Run inference in real time
- Visualize original data and ML output side-by-side
- Share insights with teams across OT and IT
How It Works
A Simple Workflow That Makes Industrial ML Work for Operational Engineers and Data Scientists
- Upload Your Model
Drag and drop your ONNX machine learning model into the Workbench. - Connect to Data
Choose real-time data streams from your SCADA system, PI System, Twin Talk, and other OT sources or Databricks, Snowflake and other IT datalakes. - Run Inference
ML Workbench applies the model to incoming data—detecting anomalies, forecasting values, or predicting failures. - Visualize and Share
Instantly compare raw vs. predicted values. Create dashboards and share results with your team or export to other tools.
Built to empower engineers, operators, and analysts—not just data scientists.
Benefits – Designed for Operational Impact
- Faster Time to Value
Apply models in minutes—not months. Go from concept to insight without infrastructure delays. - Empowers OT Teams
Let your engineers and operators validate and use ML models—no waiting on IT or Python expertise. - Closes the IT/OT Gap
Run the same model on PI data in the control room and IT data lakes like Databricks. - Accelerates Innovation
Make your AI initiatives repeatable, scalable, and easy to expand. - Reduces Downtime
Use predictive models to detect failures before they happen and act before assets go offline.
Use Cases
What Can You Do with ML Workbench? A Lot.
- Oil & Gas – Predict Equipment Failures
Detect vibration anomalies on compressors and rotating equipment using predictive models. - Power & Energy – Forecast Power Output
Use ML models to forecast megawatt production based on historical and weather data. - 🏭 Manufacturing – Improve Product Quality
Compare sensor data with expected performance metrics to reduce scrap and rework. - All Industries – Anomaly Detection
Feed temperature, pressure, or vibration signals into models that spot deviations in real time. - Decision Support for Engineers
Enable field and plant engineers to test and trust ML-driven insights—right where the data is.