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 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 Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date.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.
Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—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. At Harvard, she worked on multiple patent, publication and social network 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 and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.
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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