The Internet of Things (IoT) continues to provide value and hold promise for both the consumer and enterprise alike. To succeed, an IoT project must concern itself with how to ingest data, build actionable models, and react in real time. Chris Rawles describes approaches to addressing these concerns through a deep dive into an interactive demo centered around classification of human activities. Chris explores the guts of IoT applications and discusses the tools that will enable you to build an application like this yourself, covering the necessary components of the entire IoT stack of ingesting, storing, and processing big data—all in real time using the open source Pivotal Big Data Suite.
Chris Rawles is a data scientist at Pivotal, where he works with customers across a variety of domains, building models to derive insight and business value from their data. Prior to joining Pivotal, Chris worked in both the oil and gas and alternative energy industries, developing and utilizing computational models to identify subsurface resources. He holds an MS and BA in geophysics from UW-Madison and UC Berkeley, respectively. During his time as a researcher, Chris focused his efforts on building machine-learning-based time-series analysis tools to enable research in earthquake seismology.
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