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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 an alternative Deep Learning Framework based on python bindings (e.g. Tensorflow, MXNet) 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, not necessarily tied to an implementation of the framework will be exhibited.  

A Github Repository and Slide Deck will be made available after the session.  Some reusable monitors will be included per the training and tuning process.



Speakers
avatar for Ari Kamlani

Ari Kamlani

Deep Learning Consultant, Skymind
Data Scientist and Technology Strategist & Advisor, currently employed as a Deep Learning Consultant with Skymind and Technologist in Residence (TIR) with Techstars IoT. Previously a Data Scientist & Engineering Consultant at Otto (Tyto) for the Connected Home and Research Assis... Read More →


Saturday November 18, 2017 4:00pm - 4:40pm
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Attendees (3)