Why do businesses fail at machine learning despite its tremendous potential and the excitement it generates? Is the answer always in data, algorithms, and infrastructure, or is there a subtler problem? Will things improve in the near future? Cassie Kozyrkov shares lessons learned at Google and explains what they mean for applied data science.
Cassie Kozyrkov is Google Cloud’s chief decision scientist. Cassie is passionate about helping everyone make better decisions through harnessing the beauty and power of data. She speaks at conferences and meets with leadership teams to empower decision makers to transform their industries through AI, machine learning, and analytics. At Google, Cassie has advised more than a hundred teams on statistics and machine learning, working most closely with research and machine Intelligence, Google Maps, and ads and commerce. She has also personally trained more than 15,000 Googlers (executives, engineers, scientists, and even nontechnical staff members) in machine learning, statistics, and data-driven decision making. Previously, Cassie spent a decade working as a data scientist and consultant. She is a leading expert in decision science, with undergraduate studies in statistics and economics at the University of Chicago and graduate studies in statistics, neuroscience, and psychology at Duke University and NCSU. When she’s not working, you’re most likely to find Cassie at the theatre, in an art museum, exploring the world, playing board games, or curled up with a good novel.
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