You are viewing content from a past/completed QCon

Presentation: Panel: Predictive Architectures in Practice

Track: Predictive Architectures in the Real World

Location: Cyril Magnin I

Duration: 4:20pm - 5:00pm

Day of week: Tuesday

Share this on:

This presentation is now available to view on InfoQ.com

Watch video with transcript

Abstract

Join a panel of experts and discuss the unique challenges of building and running data architectures for predictions, recommendations and machine learning. Josh Wills built the infrastructure for Slack’s search, learning and intelligence products. Sumit Rangwala is constantly improving People You May Know recommendations for LinkedIn. Eric Chen leads the team that built Michaelangelo, massive scale soup-to-nuts machine learning infrastructure at Uber. Emily Samuels and Anil Muppalla evolved Spotify’s recommendations from batch to real-time. If you ever wondered what it really takes to build data architectures that support large-scale data products, this is the time and place to ask, learn and get inspired.

Speaker: Sumit Rangwala

Senior Staff Software Engineer - Artificial Intelligence @LinkedIn

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. 

Find Sumit Rangwala at

Speaker page

Senior Staff Software Engineer - Artificial Intelligence

Speaker: Josh Wills

Software Engineer, Search, Learning, and Intelligence @SlackHQ

Software Engineer working on Search and Learning @SlackHQ.

Find Josh Wills at

Speaker page

Speaker: Eric Chen

Tech Lead & Manager @Uber

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.

Find Eric Chen at

Speaker page

https://www.linkedin.com/in/eric-chen-a645402b/

Speaker: Emily Samuels

Staff Engineer @Spotify

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.

Find Emily Samuels at

Speaker page

https://twitter.com/emilymsa
https://www.linkedin.com/in/emsamuels/

Speaker: Anil Muppalla

Data Engineer @Spotify

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.

Find Anil Muppalla at

Speaker page

@anilmuppallar
https://www.linkedin.com/in/anilmuppalla/

2019 Tracks

2019 Schedule

Learn how to apply machine learning and artificial intelligence in engineering

Notify me when registration for QCon.ai SF 2020 opens!

Sign Up