With the increased development and adoption of streaming platforms we now have a solid mechanism for collecting and processing data in a timely fashion. Alongside that the growth and interest in machine learning and artificial intelligence has given us refined prediction and decision making.
In this session you will learn how to design a streaming application with continual learning. Jason Bell will use Kafka and DeepLearning4J to illustrate the design and implementation of a system that will accept data, apply training to a neural network and also output predictions. Jason will then look at 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.
You will learn how to:
This talk is intended for anyone with an interest in the applications that machine learning and deep learning have in an increasingly streamed world. While the focus of the talk is on the open source tools available the techniques learned from this talk can be applied to other learning and streaming platforms.
Jason Bell is a Data Engineer at Mastodon C, he specialises in high volume streaming systems, BigData solutions and also machine learning applications.
He was section editor for Java Developer’s Journal, contributed to IBM developerWorks on Autonomic Computing and authored the book “Machine Learning: Hands on for Developers and Technical Professionals”.
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