Apache Spark’s machine learning (ML) pipelines provide a lot of power, but sometimes the tools you need for your specific problem aren’t available yet. Holden Karau and Seth Hendrickson introduce Spark’s ML pipelines and explain how to extend them with your own custom algorithms. By integrating your own data preparation and machine learning tools into Spark’s ML pipelines, you’ll be able to take advantage of useful meta-algorithms like parameter searching and pipeline persistence (with a bit more work, of course).
Even if you don’t have your own machine learning algorithms that you want to implement, you’ll get an inside look at how the ML APIs are built and learn how to make even more awesome ML pipelines and customize Spark models for your needs. And if you don’t want to extend Spark ML pipelines with custom algorithms, you’ll still benefit by developing a stronger background for future Spark ML projects.
The examples will be presented in Scala, but any nonstandard syntax will be explained.
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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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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