Romeo Kienzler

IBM Center for Open Source Data and AI Technologies

Bio

Romeo Kienzler is Chief Data Scientist at the IBM Center for Open Source Data and AI Technologies (CODAIT) in San Fransisco, owning the strategy lead for AI Model Training.
He holds an M. Sc. (ETH) in Computer Science with specialisation in Information Systems, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology Zurich.
He works as Associate Professor for artificial intelligence at at the Swiss University of Applied Sciences Berne and his current research focus is on cloud-scale machine learning and deep learning using open source technologies including TensorFlow, Keras, DeepLearning4J, Apache SystemML and the Apache Spark stack.
He also contributes to various open source projects. He regularly speaks at international conferences including significant publications in the area of data mining, machine learning and Blockchain technologies.
Romeo is lead instructor of the Advance Data Science specialisation on Courera https://www.coursera.org/launch/advanced-applied-data-science-ibm with courses on Scalable Data Science, Advanced Machine Learning, Signal Processing and Applied AI with DeepLearning.
Recently his latest book on Mastering Apache Spark V2.X (http://amzn.to/2vUHkGl) has been translated into Chinese (http://www.flag.com.tw/books/product/FT363).
Romeo Kienzler is a member of the IBM Technical Expert Council and the IBM Academy of Technology - IBM’s leading brain trusts. #ibmaot

Twitter: @RomeoKienzler
Web: ibm.com

Towards de-facto standard in AI: What’s new in TensorFlow 2.0

Initially TensorFlow was just another numerical library. But the fact that it came from Google created some hype, so that everybody started to using, disregarded its flaws in usability with respect to debugability and abstraction. Since PyTorch and Keras addressed those flaws they gained a lot of ground. TensorFlow 2.0 promises to become the single go-to place for all AI questions. In this talk we’ll introduce you to the most prominent changes in TensorFlow 2.0 and how you can us them successfully in your projects.