The Systems Behind AI

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Come and hear how IBM, RedHat, Yahoo! and Taboola built massive systems to support AI use cases driven by vast amounts of data.

In collaboration with Systor 2019 - https://www.systor.org/2019/
Wednesday, June 5, 2019 @ 2:30-4:45pm.

Co-hosted with Haifa Cloud - https://www.meetup.com/Haifa-Cloud/

Agenda:

(14:00-14:30) Gathering, registration, mingling

(14:30 - 14:50) Ranit Aharonov and Yoav Katz, Project Debater: the Systems Journey, IBM
(14:50 - 15:10) Edward Bortnikov, Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix, Yahoo! Research, Verizon Media
(15:10 - 15:30) Alon Lubin, The Infrastructure Behind AI, Taboola

(15:30 - 15:50) Break + Refreshments

(15:50 - 16:10) Idan Levi, The Path Towards a Data Analysis Factory, RedHat
(16:10 - 16:45) Panel Discussion

Talks will be given in English.
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Session #1: Project Debater: the Systems Journey, IBM

Project Debater is the first AI system developed to compete in a full-live debate with a human debater. The project, an IBM Grand Challenge, is designed to build coherent, convincing speeches on its own, as well as provide rebuttals to the opponent's main arguments. In February 2019, Project Debater competed against Harish Natarajan, who holds the world record for most debate victories, in an event held in San Francisco and broadcasted live world-wide. In this talk we will tell the story of Project Debater, from it's conception to the climatic final event, with special focus on the system level and architectural challenges encountered and addressed by the team.

Session #2: Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix, Yahoo! Research, Verizon Media

Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.

These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.

Session #3: The Infrastructure Behind AI, Taboola

Session #4: The Path Towards a Data Analysis Factory, RedHat