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.
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.
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