Serving a deep learning model on a production system demands the model to be stable, reproducible, capable of isolation and to behave as a stand-alone package. One possible solution to this is a containerized microservice. Ideally, serving deep learning microservices should be quick and efficient, without having to dive deep into the underlying algorithms and their implementation. Too good to be true? Not anymore! Together, we will demystify the process of developing, training, and deploying deep learning models as a web microservice using Model Asset Exchange, an open source framework developed in Python at the IBM Center for Open Source Data and AI Technologies (CODAIT).
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