Title: Designing an ML Minded Product and a Product Minded ML System
Speaker: Grace Huang
Date: 1/23/19

Abstract
Creating a real-world machine learning product requires considerations beyond implementation of the machine learning algorithms itself. For example, very often the production environment is a constantly shifting data landscape. A well tested, carefully constructed model can become stale in a matter of days or even hours when data distribution drifts over time. In addition, a sustainable machine learning system needs to run on a healthy data ecosystem where bias is removed or accounted for as much as possible. Finally, evaluating, A/B testing, and launching machine learning product requires considerations very different from conventional product features.

In this webinar, we will share a few lessons learned from designing and maintaining a machine learning-minded product, and a product-minded machine learning system.

SPEAKER
Grace Huang, Data Science Manager, Pinterest
Grace Huang currently heads the Discovery data science team at Pinterest. Her team collaborates with engineering and product teams to create key machine learning products that power the search, recommendation, and visual discovery experience at Pinterest. In the past, Grace has led a wide range of data science projects spanning recommendation systems, search relevance, growth, and algorithm developments in genome sequencing as well as cancer diagnostics. Her passion lies in the space where algorithm meets real world application and product design. She holds a PhD in Computational Genomics from the Joint CMU-Pitt Program in Computational Biology.

MODERATOR
Ankur Teredesai, Co-Founder and CTO, KenSci; University of Washington - Tacoma; SIGKDD
Ankur M. Teredesai is the co-founder and Chief Technology Officer of KenSci. He also holds a Professorship in Computer Science & Systems at the University of Washington. Ankur's research spans data science with its applications for societal impact in healthcare. Apart from his academic appointments at RIT and the University of Washington, Teredesai has significant industry experience, having held various positions at C-DAC Pune, Microsoft Research, IBM T.J. Watson Labs, and a variety of technology startups. He has published more than 75 papers on machine learning, managed large teams of data scientists and engineers, and deployed data science solutions in healthcare. His recent applied research contributions include cost and risk prediction for readmission due to chronic conditions such as congestive heart failure. Other applications of his work have enabled predicting lengths of stay and sepsis as well as predicting medication pathways to lower risks of mortality and rehospitalization. He is Executive Director of the UW Center for Data Science, and serves as the Information Officer for ACM SIGKDD (Special Interest Group in Knowledge Discovery and Data Mining), the leading organization of industry and academic researchers in data science. He is currently an associate editor for ACM SIGKDD Explorations and IEEE Transactions on Big Data and serves on program committees of major international conferences in machine learning and healthcare.