A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, and Deep Learning Pipelines

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We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.

We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:

– As a developer how do I pick the right deep learning framework for me?

– Do I want to develop my own model or should I employ an existing one

– How do I strike a trade-off between productivity and control through low-level APIs?

In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!

Session hashtag: #DL4SAIS



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About Jules Damji

Jules S. Damji is a Developer Advocate at Databricks and an MLflow contributor. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/Loudcloud, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science (from Oregon State University and Cal State, Chico respectively), and an MA in Political Advocacy and Communication (from Johns Hopkins University).

Brooke Wenig
About Brooke Wenig

Brooke Wenig is a Machine Learning Practice Lead at Databricks. She leads a team of data scientists who develop large-scale machine learning pipelines for customers, as well as teach courses on distributed machine learning best practices. She is a co-author of Learning Spark, 2nd Edition, co-instructor of the Distributed Computing with Spark SQL Coursera course, and co-host of the Data Brew podcast. She received an MS in Computer Science from UCLA with a focus on distributed machine learning. She speaks Mandarin Chinese fluently and enjoys cycling. [daisna21-speakers]