Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Schedule: Streaming sessions

9:0017:00 Tuesday, 23 May 2017
Location: London Suite 2/3
Angie Ma (Faculty), Ben Lorica (O'Reilly), Ira Cohen (Anodot), Yingsong Zhang (ASI Data Science), Ali Hürriyetoglu (Statistics Netherlands), Nelleke Oostdijk (Radboud University), Robin Senge (inovex), Mathew Salvaris (Microsoft), Miguel Gonzalez-Fierro (Microsoft), Amitai Armon (Intel), Yahav Shadmi (Intel), Kay Brodersen (Google), Ding Ding (Intel), Alan Mosca (nPlan), Eduard Vazquez (Cortexica Vision Systems), Aida Mehonic (The Alan Turing Institute), David Barber (UCL)
A full day of hardcore data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. Along the way, leading data science practitioners teach new techniques and technologies to add to your data science toolbox. Read more.
17:2518:05 Wednesday, 24 May 2017
Level: Beginner
Dr.-Ing. Michael Nolting (Volkswagen Commercial Vehicles)
Average rating: *....
(1.67, 6 ratings)
It is nearly impossible to sample enough training data initially to prevent autonomous driving accidents on the road, as has been sadly proven by Tesla’s autopilot. Michael Nolting explains that to overcome this problem, a real-time system has to be created to detect dangerous runtime situations in real time, a process much like website monitoring. Read more.
16:3517:15 Thursday, 25 May 2017
Level: Intermediate
Kamran Yousaf (Redis Labs)
Average rating: ***..
(3.50, 6 ratings)
Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms. Read more.