Machine learning is a powerful approach to surfacing hidden value from a vast sea of a company’s data assets. Yet going all the way from raw data to actionable insight presents a number of practical challenges. Data often need substantial pre-processing and the resulting models need to be scaled to growing amounts of data and deployed into production data pipelines and applications. In this talk we discuss how many of these automation issues are being leap-frogged with emerging technologies, both algorithmic and framework in nature. Scalable, robust, ease-of-use, accurate, and reproducible results are the key characteristics of what will define success in this exciting new space. Examples from real-world machine learning projects will be used to drive the discussion.
Henrik Brink is CTO and co-founder of wise.io. He has worked in industry and academia designing and implementing large-scale distributed software projects used in production around the globe. He has previously founded and managed a consultancy business focused on scalable backend systems and modern web technologies. Henrik has a Physics degree from University of Copenhagen and has worked as a researcher and data scientist at UC Berkeley. He has published papers in the intersection of software development and machine learning on real-world messy data, and has a good handle on the latest developments in software development, machine learning and distributed architectures.
Joshua Bloom is CEO and co-founder of wise.io. He is an astronomy professor at the University of California, Berkeley where he teaches astrophysics and Python for data science. He has been a Sloan Fellow, Junior Fellow at the Harvard Society, and Hertz Foundation Fellow. In 2010, he was awarded the Pierce Prize from the American Astronomical Society. He has published over 250 refereed academic articles. Josh holds a PhD from Caltech and degrees from Harvard and Cambridge. He serves on the Berkeley Startup Cluster Advisory Committee.
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