Presentation
Applying a Task-Based Approach to Distributed Machine Learning Workflows
DescriptionThe growing demands across various scientific fields
have led to a significant shift in applications that consume data at
the edge of the computing continuum. These applications require
unified programming models for the composition of components
and coordinating the execution of computational workloads,
including training machine learning (ML) models on distributed
resources. Personalized healthcare often leverages data generated
from wearable devices used to train ML models, can be benefited
from distributed computing approaches. Specifically, stroke care
can be greatly benefited from distributed ML with modifiable
risk factors that can be monitored using wearable devices. In this
work, we present an implementation that leverages distributed
techniques for large-scale ML workflows using electrocardiogram
(ECG) recordings for atrial fibrillation (AF) classification. The
application was evaluated using the PhysioNet database, show-
casing the potential of distributed, ML in stroke care, opening
the way for future creation of more advanced models embedded
in edge devices.
have led to a significant shift in applications that consume data at
the edge of the computing continuum. These applications require
unified programming models for the composition of components
and coordinating the execution of computational workloads,
including training machine learning (ML) models on distributed
resources. Personalized healthcare often leverages data generated
from wearable devices used to train ML models, can be benefited
from distributed computing approaches. Specifically, stroke care
can be greatly benefited from distributed ML with modifiable
risk factors that can be monitored using wearable devices. In this
work, we present an implementation that leverages distributed
techniques for large-scale ML workflows using electrocardiogram
(ECG) recordings for atrial fibrillation (AF) classification. The
application was evaluated using the PhysioNet database, show-
casing the potential of distributed, ML in stroke care, opening
the way for future creation of more advanced models embedded
in edge devices.
