Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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

4:40pm5:20pm Thursday, March 28, 2019
Holden Karau (Independent), Rachel Warren (Salesforce Einstein)
Average rating: ****.
(4.60, 5 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 (a.k.a. tuning) or our jobs may be eaten by Cthulhu. Holden Karau and Rachel Warren explore auto-tuning jobs using systems like Apache BEAM, Mahout, and internal Spark ML jobs as workloads—including new settings in 2.4. Read more.