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With the increased development and adoption of streaming platforms, you 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 you refined prediction and decision making. Jason Bell will share with you an overview of a self-learning knowledge system that uses Clojure, Kafka and Deeplearning4j to accept data, apply training to a neural network, and output predictions. Jason will cover 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 will explore 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.
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
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Jason Bell is a data engineer and startup founder based in Northern Ireland. He authored the book Machine Learning: Hands on for Developers and Technical Professionals. An in demand speaker and panelist, Jason has previously spoken at the Strata Data Conference in London on Kafka and AI technologies, the Digital DNA conference on AI and spends his spare time studying supply and demand pricing on different asset types (yes he does this for fun).