Learning how to design automatically updating AI with Apache Kafka and Deeplearning4j
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.
| Talk Title | Learning how to design automatically updating AI with Apache Kafka and Deeplearning4j |
| Speakers | Jason Bell (Independent Speaker) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | May 22-24, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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:
