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Presentation: Serverless for Data Science

Track: Handling Sequential Data Like an Expert / ML Applied to Operations

Location: Cyril Magnin II

Duration: 12:45pm - 12:55pm

Day of week: Wednesday

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Abstract

In this talk we'll first see the basic idea behind serverless cloud architecture and learn how to deploy a very simple web application to AWS Lambda using Zappa. We'll then look in detail at the embarrassingly parallel data science problems where serverless really shines. In particular we'll take a look at PyWren, an ultra-lightweight alternative to heavy big data distributed systems such as Spark.

Host: Mike Lee Williams

Research engineer @Cloudera Fast Forward Labs

Mike Lee Williams does applied research into computer science, statistics and machine learning at Cloudera Fast Forward Labs. While getting his PhD in astrophysics he spent 2% of his time observing the heavens in beautiful far west Texas, and the other 98% trying to figure out how to fit straight lines to data. He once did a postdoc at the Max Planck Institute for Extraterrestrial Physics, which, amazingly, is a real place.

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2019 Tracks

  • Groking Timeseries & Sequential Data

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

  • Deep Learning in Practice

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