3 days / 40+ speakers
12 workshops

May 17-19, 2017 | Vilnius, Lithuania
IBM Watson IoT, Switzerland

Romeo Kienzler

Romeo Kienzler works as Chief Data Scientist in the IBM Watson IoT World Wide team helping clients to apply advanced machine learning at scale on their data. His current research focus is on scalable machine learning on Apache Spark. He is contributor to ApacheFlink and recently started to contribute to DeepLearning4J and Apache SystemML by accelerating computations on GPUs.


Realtime- Cognitive IoT using DeepLearning and Online Learning on top of ApacheSpark Streaming

DeepLearning frameworks are popping up at very high frequency but only a few of them are suitable to run on clusters, use GPUs and supporting topologies beyond Feed-Forward at the same time. DeepLearning4J features all this without forcing you to learn new exotic programming languages and in addition also scales-out on well established infrastructures like ApacheSpark and Hadoop/YARN.

In this talk we will introduce DeepLearning4J on top of ApacheSpark with an example to create an anomaly detector for IoT sensor data with a LSTM auto encoder neural network.





Exploratory IoT Sensor data Analysis with ApacheSpark, Python and matplotlib

In this workshop you will learn how to capture, process, store and analyse IoT sensor data in realtime using edge devices like the Raspberry Pi, MQTT, Cloud, ApacheSpark, python and matplotlib. This workshop is based on my 4 weeks course on courser: https://www.coursera.org/learn/exploring-visualizing-iot-data

Workshop Format: Half-Day hands-on workshop using your own laptop.