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UBER : Big Data Infrastructure and Machine Learning Platform

Jan 16 - 17, 2019 · Toronto, Canada

This is to livestream the tech talks host in San Francisco. Join us online, you can listen, watch, Q&A with speakers from anywhere around the world.
Sign up here:https://learn.xnextcon.com/event/eventdetails/W19011618

Details:
Modern day Data Infrastructure and Machine Learning Platforms are important foundations that help to support company's future growth.

6:40 pm --- 7:30 pm Talk 1: Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Uber’s mission is to ignite opportunities by setting the world in motion. To fulfill this mission, Uber relies heavily on making data-driven decisions in every product area and we need to store and process an ever-increasing amount of data, in addition to providing faster, more reliable, and more-performant access.

This talk will reflect on the challenges faced with scaling Uber’s Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing our hardware. We will provide a behind-the-scenes look at the current data technology landscape at Uber, including various open-source technologies (e.g. Hadoop, Spark, Hive, Presto, Kafka, Avro) as well as open-sourced in-house-built solutions such as Hudi, Marmaray, etc. We'll dive into the technical aspects of how our ingestion platform was re-architected to bring in 10+ trillion events/day, with 100+ TB new data/day, at minute-level latency, how our storage platform was scaled to reliably store 100+ PB of data in the data lake, and our processing platform was designed to efficiently serve millions of queries and jobs/day while processing 1+ PB per day. You’ll leave the talk with greater insight into how data truly powers each and every Uber experience and will be inspired to re-envision your own data platform to be more extensible and scalable.

Speaker : Reza Shiftehfar (Uber)

7:30 pm -- 8:20 pm Talk2 : Michelangelo PyML - Uber’s Platform for Rapid Python ML Model Development

Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, hundreds of data scientists, engineers, product managers, and researchers work on ML solutions across the company. This talk will cover a brief history of Uber's machine learning platform - Michelangelo. We will take a closer look into a model life-cycle of prototyping, validation, and productionization and the importance of frictionless experience at each stage of this process. And finally, we will focus on PyML - a new extension of Michelangelo that enables faster Python ML model development and seamless integration with Uber's production infrastructure.

Speaker: Stepan Bedratiuk (Uber)

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Event organizers
  • Toronto AI Tech Talks Group

    "Learn by Practice". AI Tech Talks Group is a global tech community to learn and practice AI tech together with thousands tech engineers by tech talks, networking, workshop, code labs, hackathon, conference, training, etc.. we invite tech leads from companies like Microsoft, Amazon, Google, Facebook, Uber, Airbnb, Pinterest, Twitter, Nvidia, etc...and successful startups to talk and share their practices, practical experiences and solutions to solve engineering problems. We focus on four areas: • AI tech:

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