Lessons learned from building large ML systems
Who is this presentation for?Data scientists or analysts
Alice Zheng teaches four lessons learned from building and operating production-grade machine learning systems that interact with other complex production components as well as the end customer. You’ll learn about simplicity over complexity, experimentation, metrics and evaluation, and monitoring and diagnosis. You may recognize many of the lessons and walk away with new inspiration or conceptual frameworks for the problems you face every day. If you’re a new practitioner, you’ll gain practical advice that may help you in your future endeavors.
- Familiarity with machine learning and the technical production environment (useful but not required)
What you'll learn
- Learn that simplicity >> complexity, experimentation is hard, evaluation is hard, and monitoring is hard
Alice Zheng is a senior manager of applied science on the machine learning optimization team on Amazon’s advertising platform. She specializes in research and development of machine learning methods, tools, and applications. She’s the author of Feature Engineering for Machine Learning. Previously, Alice has worked at GraphLab, Dato, and Turi, where she led the machine learning toolkits team and spearheaded user outreach; was a researcher in the Machine Learning Group at Microsoft Research – Redmond. Alice holds PhD and BA degrees in computer science and a BA in mathematics, all from UC Berkeley.
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