Modern day Data Infrastructure & Machine Learning Platforms are important foundations that help to support company's future growth.
We are lucky & excited to have two talks from Uber: The first talk is on Uber's Big Data Platform, & 2nd talk on Machine Learning Platform : Michelangelo PyML. This is the event you don't want to miss.

Agenda:
6 pm -- 6:30 pm Check-in, networking + food
6:35 pm -- 6:40 pm Introduction
6:40 pm --- 7:30 pm Talk 1
7:30 pm -- 8:20 pm Talk 2
8:20 pm --- 9 pm Closing
9 pm -- Office closed.

Talk 1: Ubers Big Data Platform: 100+ Petabytes with Minute Latency

Ubers 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 & we need to store & process an ever-increasing amount of data, in addition to providing faster, more reliable, & more-performant access.

This talk will reflect on the challenges faced with scaling Ubers Big Data Platform to ingest, store, & 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, & our processing platform was designed to efficiently serve millions of queries & jobs/day while processing 1+ PB per day. Youll leave the talk with greater insight into how data truly powers each & every Uber experience & will be inspired to re-envision your own data platform to be more extensible & scalable.

Speaker : Reza Shiftehfar (Uber)
Reza Shiftehfar currently leads Ubers Hadoop Platform team. His team helps build & grow Ubers reliable & scalable Big Data platform that serves petabytes of data utilizing technologies such as Apache Hadoop, Apache Hive, Apache Kafka, Apache Spark, & Presto. Reza is one of the founding engineers of Ubers data team & helped scale Uber's data platform from a few terabytes to over 100 petabytes while reducing data latency from 24+ hours to minutes. Reza holds a Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign.

Talk2 : Michelangelo PyML - Ubers Platform for Rapid Python ML Model Development

Uber aims to leverage machine learning (ML) in product development & the day-to-day management of our business. In pursuit of this goal, hundreds of data scientists, engineers, product managers, & 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, & productionization & 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 & seamless integration with Uber's production infrastructure.

Speaker: Stepan Bedratiuk (Uber)
Stepan Bedratiuk is a lead engineer on Michelangelo's PyML team. His work focused on scaling model deployment pipelines & model serving services. Prior to ML platform team, Stepan worked on Uber's data platform team & helped to unify & scale the data access layer. Stepan holds B.S. & M.S. in Applied Mathematics from the Taras Shevchenko National University of Kyiv, Ukraine.