In this demo, we will showcase how to build an end-to-end ML-powered ‘data to outcome’ application for data at scale, using a drag and drop approach.
We will demonstrate this using a manufacturing use case; but the challenges, solution, and benefits are applicable to all domains.
The customer, an automotive manufacturer in this case, needed to ascertain when a particular machine in their production line was going to fail, to schedule maintenance or replacement accordingly and prevent any failures, downtimes, and losses.
In this data to outcome demo, we’ll show how to:
- COLLECT: Ingest batch and real-time telemetry data from their machines and equipment
- TRANSFORM: Create a pipeline to integrate, transform and prepare the data
- PREDICT: Infuse ML into the pipeline to predict the Remaining Useful Life (RUL) of a machine based on volt, pressure, rotation, and vibration data
- RECOMMEND: Build supercharged dashboards with real-time actionable insights and ML-powered recommendations
- ACT: Take actions, such as scheduling maintenance, from the same dashboard
- MONITOR: Use the same integrated dashboard to identify potential failures in machines before they occur
- AUTOMATE: Automate time-consuming business processes and workflows
And, accomplish everything, from a unified platform, without having to write a single line of code.
Gathr.ai’s capabilities on display in this demo
- Unified UX
- Drag and drop UI
- 300+ pre-built connectors
- 300+ pre-built transformations
- ML at scale
- Predictive analytics
- Insights delivery
- Process Automation
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GathrIQ : Data+AI Copilot
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