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Saturday, November 18 • 4:00pm - 4:40pm
Scaling From Research to Production with Skymind DL4J and ScalNet

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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.  


avatar for Ari Kamlani

Ari Kamlani

AI Technology Strategist and Architect

Saturday November 18, 2017 4:00pm - 4:40pm PST