Accelerating Apache Big Data and Machine Learning Workloads on OpenPOWER

DON'T FORGET TO RSVP TO THIS MEETUP!!!

With the evolution of Data Science workloads at scale, compute and storage architectures are also evolving to deliver new levels of processing power that continue to push the boundaries of what’s possible with data. Custom silicon technologies like GPUs built originally for graphics rendering or FPGAs for digital signal processing are making their way from research and scientific computing clusters to the commercial data center, where the Apache Big Data (Apache Hadoop, Apache Spark, Apache Hive, ...) community is already adapting existing frameworks to take advantage of breakthrough speed-ups for computing and analyzing as well as moving and storing data. See how these technologies are pushing advancements in today’s killer app - machine and deep learning - and how you can get started now in the Apache ecosystem.

Come meet other developers, data engineers and data scientists and hang out with IBM and Hortonworks engineers to get for a taste of these new tools (and beer & bites!), plus cool giveaways!

Agenda

NEW TOPIC • Big Data Big Learning: Machine & Learning & Deep Learning with Hadoop

• Build FAST Deep Learning Apps with Docker on OpenPOWER and GPUs - Indrajit Poddar, STSM, Quantum Computing and POWER Cloud Initiatives, IBM

• Crail: A High-Performance I/O Architecture for the Apache Data Processing Ecosystem - Bernard Metzler, Technical Leader High Performance I/O, IBM Research Zurich

• Accelerated Hadoop Erasure Coding with OpenPOWER CAPI and the CAPI SNAP Framework - Joerg-Stephan Vogt, FPGA Accelerator Development, IBM

• Intro to Watson Machine Learning - Khadija Souissi, Certified IT Specialist, IBM

Join us on April 4th! See you there!

WANT TO ATTEND DATAWORKS SUMMIT? GET YOUR DISCOUNT TICKET HERE.

DISCOUNT TICKET TO DATAWORKS SUMMIT (Formerly Hadoop Summit).

Held in Munich - April 4th to 6th.

Get 20% off using discount code MSPO20 here:

https://dataworkssummit.com/munich-2017/attend/