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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

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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. 

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Speaker: Josh Wills

Software Engineer, Search, Learning, and Intelligence @SlackHQ

Software Engineer working on Search and Learning @SlackHQ.

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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.

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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.

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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.

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2019 Tracks

  • Sequential Data: Natural Language, Time Series, and Sound

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.