Streaming machine learning is being integrated in Spark 2.1, but you don’t need to wait. Holden Karau and Seth Hendrickson demonstrate how to do streaming machine learning using Spark’s new Structured Streaming and walk you through creating your own streaming model. Holden and Seth will also cover how to use structured machine-learning algorithms (if they are merged by the talk). By the end of this session, you’ll have a better understanding of Spark’s Structured Streaming API as well as how machine learning works in Spark.
Holden Karau is a transgender Canadian open source developer advocate at Google focusing on Apache Spark, Beam, and related big data tools. Previously, she worked at IBM, Alpine, Databricks, Google (yes, this is her second time), Foursquare, and Amazon. Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date. She is a committer on the Apache Spark, SystemML, and Mahout projects. When not in San Francisco, Holden speaks internationally about different big data technologies (mostly Spark). She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal. Outside of work, she enjoys playing with fire, riding scooters, and dancing.
Seth Hendrickson is a top Apache Spark contributor and data scientist at Cloudera. He implemented multinomial logistic regression with elastic net regularization in Spark’s ML library and one-pass elastic net linear regression, contributed several other performance improvements to linear models in Spark, and made extensive contributions to Spark ML decision trees and ensemble algorithms. Previously, he worked on Spark ML as a machine learning engineer at IBM. He holds an MS in electrical engineering from the Georgia Institute of Technology.
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