Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Learning how to design automatically updating AI with Apache Kafka and Deeplearning4j

Jason Bell (MastodonC)
16:3517:15 Thursday, 24 May 2018

Who is this presentation for?

  • Data engineers, software developers, and IT analysts

Prerequisite knowledge

  • Basic programming experience

What you'll learn

  • Learn the core elements of designing a self-learning prediction system using streaming tools and artificial intelligence

Description

With the increased development and adoption of streaming platforms, we now have a solid mechanism for collecting and processing data in a timely fashion. The growth and interest in machine learning and artificial intelligence has also given us refined prediction and decision making.

Jason Bell offers an overview of a self-learning knowledge system that uses Apache Kafka and Deeplearning4j to accept data, apply training to a neural network, and output predictions. Jason covers the system design and the rationale behind it and the implications of using a streaming data with deep learning and artificial intelligence. Along the way, Jason explores the considerations that have to be made on how this application can continually learn, when manual intervention is required, and how to evaluate incremental learning.

Topics include:

  • Planning the system
  • Using Kafka Connect to store raw streaming data
  • Defining a Deeplearning4j neural network
  • Reapplying neural network training with new training data
  • Making predictions using the Kafka Streaming API
Photo of Jason Bell

Jason Bell

MastodonC

Jason Bell is a machine learning engineer at Mastodon C specializing in high-volume streaming systems, big data solutions, and machine learning applications. Jason was section editor for Java Developer’s Journal, has contributed to IBM developerWorks on autonomic computing, and is the author of Machine Learning: Hands On for Developers and Technical Professionals.