Holden Karau, Francesca Lazzeri, and Trevor Grant offer an overview of Kubeflow and walk you through using it to train and serve models across different cloud environments (and on-premises). You’ll use a script to do the initial setup work, so you can jump (almost) straight into training a model on one cloud and then look at how to set up serving in another cluster/cloud.
The first part of the session will involve training a model on either Google Kubernetes Engine or on your own laptop using Minikube (as desired); in the second part, you’ll take the trained model and deploy it to your choice of Google’s, Amazon’s, Microsoft’s, or IBM’s cloud and make it publicly accessible to real traffic.
To keep the course simple, you’ll focus on training on a simple mode. If you speed through everything, you can either keep deploying more more clouds (gotta catch ‘em all) or try training a more complex, more realistic model doing feature preprocessing (like GitHub issue classification).
Note: Accounts will be provided for Google’s and Microsoft’s cloud, but users of other clouds will have to use their own accounts.
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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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.
Francesca Lazzeri is a machine learning scientist on the cloud advocacy team at Microsoft. An expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems, she has worked with these issues in a wide range of industries, including energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit and worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities in USA and Europe. and is a mentor for PhD and postdoc students at the Massachusetts Institute of Technology. She enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding. Francesca holds a PhD in innovation management.
Trevor Grant is an open source technical evangelist at IBM. He’s also a committer on the Apache Mahout project and a contributor to the Apache Streams (incubating), Apache Zeppelin, and Apache Flink projects. In former roles, he called himself a data scientist, but the term is so overused these days that he stopped. Trevor is an organizer of the newly formed Chicago Apache Flink Meetup and has presented at Flink Forward, ApacheCon, Apache Big Data, and other meetups nationwide. Trevor was a combat medic in Afghanistan in 2009 and wrote an award-winning undergraduate thesis between missions. He holds an MS in applied math and an MBA from Illinois State University. He has a dog and a cat and a ’64 Ford, and he loves them all very much.
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