Executive Briefing: Usable machine learning—Lessons from Stanford and beyond
Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis details the lessons learned in building new systems and interfaces to help users quickly and easily leverage the data at their disposal with production experience from Facebook, Microsoft, and the Stanford DAWN project.
Drawing on his research and startup experience, Peter examines why deep networks aren’t a panacea for most organizations’ data; how usability and speed are the best path to better models; why Facebook, Apple, Amazon, Netflix, and Google (FAANG) likely won’t (and can’t) dominate every vertical; and why automating feature selection is more practical than AutoML.
- General knowledge of how machine learning models are built, trained, and deployed
What you'll learn
- Learn practical principles informed by recent theory and actual production deployments
- Understand how to use the existing body of structured enterprise data as a source of training data, focus on augmentation and not automation of common workflows, and deploy models quickly and rapidly iterate
Sisu | Stanford University
Peter Bailis is the founder and CEO of Sisu, a data analytics platform that helps users understand the key drivers behind critical business metrics in real time. Peter is also an assistant professor of computer science at Stanford University, where he coleads Stanford DAWN, a research project focused on making it dramatically easier to build machine learning-enabled applications. He holds a PhD from the University of California, Berkeley, for which he was awarded the ACM SIGMOD Jim Gray Doctoral Dissertation Award, and an AB from Harvard College in 2011, both in computer science.
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