Misha Bilenko, Principal Researcher, Microsoft @ MLconf SEA
summary
Covers practical scaling challenges in machine learning beyond large training sets: handling extremely high-dimensional feature spaces, managing many components in predictive pipelines, and supporting collaboration among multiple data scientists on shared experiments. Discusses approaches and considerations for scaling algorithms, systems, and workflows to address these specific dimensions of "big learning."
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