Presented By O'Reilly and Cloudera
December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference
Singapore

Spark Structured Streaming for machine learning

Holden Karau (Independent), Seth Hendrickson (Cloudera)
12:05pm–12:45pm Thursday, December 8, 2016
Spark & beyond
Location: 321/322 Level: Intermediate
Tags: streaming
Average rating: ****.
(4.67, 6 ratings)

Prerequisite Knowledge

What you'll learn

  • Understand the basics of Structured Streaming and streaming machine learning

Description

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.

Photo of Holden Karau

Holden Karau

Independent

Holden Karau is a transgender Canadian software working in the bay area. Previously, she worked at IBM, Alpine, Databricks, Google (twice), 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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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’s 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.

Photo of Seth Hendrickson

Seth Hendrickson

Cloudera

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.