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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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