You’ve managed to train and deploy an edge AI model, but now you need to organize all of the data and telemetry produced across many distributed systems. How can you do this? In this webinar, we’ll show you how.
As edge AI moves into production, traditional cloud-first data architectures can quickly become expensive and difficult to scale. Industrial systems may generate huge volumes of raw data, operate across constrained or unreliable networks, and face strict requirements around where sensitive data can be stored and processed.
Join us with Expanso for a practical engineering session exploring how distributed data pipelines can process, filter and manage data directly at the edge, before deciding what actually needs to move to the cloud.
During the session, we’ll examine the limitations of traditional centralised data architectures and demonstrate how the Expanso Framework can be deployed across edge infrastructure to keep raw datasets local, reduce unnecessary data movement and maintain data operations when connectivity is interrupted.
You’ll also see a live demonstration covering how to:
• Deploy and configure a distributed edge data pipeline
• Ingest and process data locally at the edge
• Define filtering rules to determine what data should be retained, discarded or transferred
• Keep raw datasets on-site while sending selected telemetry and results to the cloud
• Maintain data processing during network outages
• Monitor distributed systems and data pipelines in real time
• Support data sovereignty, privacy, and regulatory requirements across geographically distributed deployments
This session will provide a practical look at the data architecture required to take edge AI from individual deployments to production-scale fleets.
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Free