Presentation: Panel: Predictive Architectures in Practice

Sumit Rangwala is a Senior Staff Software Engineer, Artificial Intelligence, currently focusing on building scalable machine learning infrastructure at Linkedin. Over the last 15+ years, Sumit has built technologies ranging from computer networking protocol, smart grid, distributed K-V store, ML scoring library, and graph recommendation platform. Sumit earned his Masters and PhD from University of Southern California focusing on computer networking and distributed systems. 

Software Engineer working on Search and Learning @SlackHQ.

Eric Chen leads the offline model processing pipelines and model online and offline serving accuracy of Michelangelo ML Platform at Uber, driving several projects such as customizble workflows, customizable transformers and model training across multiple computing environments. Prior to Uber, he worked on search quality and maps in Google.

Emily Samuels is a Staff Engineer at Spotify. Her current focus is improving the recommendations on the Home Tab. In the past she has worked on Playlist Recommendations, the Discover Page, and Radio. She graduated from the University of Michigan with a BS in computer science. Emily has worked in the technology industry for ten years and was previously with FactSet Research Systems and Goldman Sachs. Her main interest is big data and she enjoys working on batch and streaming pipelines and distributed databases.

Anil Muppalla is a Data Engineer at Spotify. His current focus building content recommendations on the Home Tab. In the past he has worked on real time data infrastructure for Spotify. He graduated from Georgia Tech with a MS in computer science. His main interest is in solving batch and streaming data problems and data infrastructure in general.