Use case · Edge machine learningBring data processing to your ML workloads
Expanso runs data preparation and routing jobs on the nodes where inputs are created. Your model runtime, your hardware: Expanso handles the pipeline around them.
What a distributed ML evaluation has to prove
A successful pipeline run does not by itself prove model accuracy, hardware suitability, or production reliability. Three things need evidence.
Data movement
Shipping every raw input to a central system adds network dependency and transfer cost. Prepare and aggregate at the source instead.
Site variability
Hardware, connectivity, and workload behavior differ by location. Each site needs its own measurements, not a lab average.
Operational ownership
Someone owns the model runtime, data handling, rollback, and monitoring. A good evaluation makes that split explicit.
A bounded evaluation, in four steps
Prove the model runtime and hardware with representative inputs, independent of Expanso. Model performance stays your validation lane.
Use an Expanso job to prepare, route, or aggregate inputs and outputs on the nodes you select.
Pull the plug on purpose. Observe buffering, retry, recovery, and output handling under representative connectivity loss.
Record application latency, throughput, resource use, output quality, and operating cost. Accept on numbers, not demos.
Three lanes of acceptance evidence
Keep the lanes separate so a green light in one never stands in for the others.
The model and hardware path proves out on representative inputs.
Data preparation, routing, outputs, and failure behavior prove out on your nodes.
Accept only after end-to-end measurements in your own environment.
Where teams run this
Manufacturing →
Prepare and route inspection inputs and outputs on the factory floor.
Remote operations
Measure workflow behavior through intermittent connectivity.
Distributed sensors
Aggregate locally and move only selected outputs upstream.
Bring a model runtime and a data flow
We'll help you define a bounded pipeline evaluation, without assuming latency, savings, or compliance results in advance.
