DeepLearning4J (Deep Learning for Java - DL4J, inception 2013) was specifically designed with Enterprise and Production in mind, as a first-class citizen to the JVM. Skymind develops and maintains the complete DL4J stack and the abstraction for Scala (ScalNet) with a focal point on scalability and vendor integrations.
This session will focus on the challenges in migrating a research prototype to a more production ready system within the JVM. Specifically, migrating/importing an alternative Deep Learning Framework based on python bindings (e.g. Keras via Tensorflow) to DL4J/ScalNet within a distributed environment using Apache Spark.
A walkthrough of a temporal IoT use case modeling an LSTM Network demonstrating the different phases of a project will be shown. Furthermore, the different workflow capabilities in crossing the language boundaries.
