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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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 Senior Machine Learning Scientist on the cloud developer advocacy team at Microsoft. Francesca has multiple years of experience as a data scientist and data-driven business strategy expert; she is passionate about innovations in big data technologies and the applications of machine learning–based solutions to real-world problems. Her work on these issues covers 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 is currently a mentor for PhD and postdoc students at the Massachusetts Institute of Technology and enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding.
Trevor Grant is PMC Member of the Apache Mahout and Apache Streams projects. He is a tinker extraordinaire and does a poor job of documenting his projects on www.rawkintrevo.org. He has an M.S. of Applied Math, a dog, a cat, an M.B.A., and a home in Chicago. He speaks a fair amount at locations internationally, and in general, his talks are usually pretty fun.
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