Data science, machine learning, and artificial intelligence have exploded in popularity in the last five years, but the nagging question of how to put models into production remains. Engineers are typically tasked to build one-off systems to serve predictions that must be maintained amid a quickly evolving backend serving space that has evolved from single machine to custom clusters to “serverless” to Docker to Kubernetes.
Holden Karau and Trevor Grant present Kubeflow—an open source project that makes it easy for users to move models from laptop to ML rig to training cluster to deployment—and demonstrate how to build a machine learning model and set up serving across clouds.
Holden Karau is a transgender Canadian software engineer 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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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.
Trevor Grant is a computer nerd at IBM, an Apache Software Foundation Member, and is involved in multiple projects such as Mahout, Streams, and SDAP-incubating, just to name a few. He speaks about computer stuff internationally. He’s taken numerous classes in stand-up and improv comedy to make his talks more pleasant for you—the listener. He holds an MS in applied math and an MBA from Illinois State University.
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