Practical Machine Learning Pipeline using Streaming IOT Sensor Data, Matthiew Dumoulin and Mateusz Dymczyk, Big Data Analytics Tokyo 2017

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Talk given by  Mathieu Dumoulin at Big Data Analytics Tokyo 2017 and published on February 8, 2017
Real-time IoT sensor data collected from on a working, moving robot is used to predict failure using anomaly detection with autoencoders with H2O, all running on the MapR Converged Platform and MapR Streams (a Kafka superset implementation). This drives an AR headset to visualize the results (green OK red FAILURE) on a marker attached to the robot.

Demo video:

Source code: GitHub - mdymczyk/iot-pipeline