Name: Using DJL to Deploy a Pre-Trained Deep Learning Model from Apache Kafka®
Start: 2021-03-03T16:00:00-06:00
End: 2021-03-03T17:00:00-06:00

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Agenda:
4:00pm-4:05pm: Online Networking
4:05pm-4:50pm: Lai Wei, AWS + Stanislav Kirdey, Netflix (Q&A included)
4:50pm: Log in or Join our Forum (forum.confluent.io) to ask any follow up questions!

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Speakers: Lai Wei, AWS + Stanislav Kirdey, Netflix

Talk: Using DJL to deploy a pre-trained deep learning model directly from Apache Kafka®

Abstract: Leading streaming users such as Netflix and Amazon music use open source library DJL to deploy deep learning models directly from Scala-based streaming applications. Deep Java Library (djl) is an open source library designed to let you build and deploy AI models within the Java/Scala ecosystem. In this talk, we will give a high-level overview of DJL architecture and main features. Then we will show how data engineers with minimum data science experience can make use of the latest and greatest open source AI models by using DJL to deploy a pre-trained deep learning model directly from Kafka. We can end with a customer story (Netflix) on deploying pre-trained deep learning models from streaming applications for real-time and large scale inference.

Bios:
Lai Wei is a Software Engineer with AWS Deep Learning team. He is focusing on building easy to use, high-performance and scalable deep learning frameworks for engineers and data scientists. Outside of work, he enjoys spending time with his son, hiking, and skiing.

Stanislav Kirdey works at Netflix where he focuses on large-scale, applied machine learning in the areas of NLP, search, and anomaly detection. Outside of machine learning and work, Stanislav is obsessed with RuPaul’s Drag Race, POSE, and Star Trek.

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Using DJL to Deploy a Pre-Trained Deep Learning Model from Apache Kafka®

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Kansas City Apache Kafka® Meetup by Confluent

Using DJL to Deploy a Pre-Trained Deep Learning Model from Apache Kafka®

Kansas City Apache Kafka® Meetup by Confluent

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