Speaker
Description
The DEEP-Hybrid-DataCloud project researches on intensive computing
techniques such as deep learning, that require specialized GPU hardware
to explore very large datasets, through a hybrid-cloud approach that
enables the access to such resources. DEEP is built on User-centric
policy, i.e. we understand the needs of our user communities and help
them to combine their services in a way that encapsulates technical
details the end user does not have to deal with. DEEP takes care to
support users of different levels of experience by providing different
integration paths. We show our current solutions to the problem, which
among others include the Open Catalog for deep learning applications,
DEEP-as-a-Service API for providing web access to machine learning
models, CI/CD pipeline for user applications, Testbed resources. We also
present our use-cases tackling various problems by means of deep
learning and serving to demonstrate usefulness and scalability of our
approach.
