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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Rachel Warren

Rachel Warren
Data Scientist, Salesforce Einstein

Website | @warre_n_peace

Rachel Warren is a software engineer and data scientist for Salesforce Einstein, where she is working on scaling and productionizing auto ML on Spark. Previously, Rachel was a machine learning engineer for Alpine Data, where she helped build a Spark auto-tuner to automatically configure Spark applications in new environments. A Spark enthusiast, she is the coauthor of High Performance Spark. Rachel is a climber, frisbee player, cyclist, and adventurer. Last year, she and her partner completed a thousand-mile off-road unassisted bicycle tour of Patagonia.

Sessions

11:20am–12:00pm Thursday, 09/13/2018
Location: 1A 21/22 Level: Intermediate
Holden Karau (Independent), Rachel Warren (Salesforce Einstein), Anya Bida (Salesforce)
Average rating: ****.
(4.00, 2 ratings)
Apache Spark is an amazing distributed system, but part of the bargain we've made with the infrastructure deamons involves providing the correct set of magic numbers (aka tuning) or our jobs may be eaten by Cthulhu. Holden Karau, Rachel Warren, and Anya Bida explore auto-tuning jobs using systems like Apache BEAM, Mahout, and internal Spark ML jobs as workloads. Read more.