Data Engineering Podcast


This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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25 February 2019

Deep Learning For Data Engineers - E71

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Summary

Deep learning is the latest class of technology that is gaining widespread interest. As data engineers we are responsible for building and managing the platforms that power these models. To help us understand what is involved, we are joined this week by Thomas Henson. In this episode he shares his experiences experimenting with deep learning, what data engineers need to know about the infrastructure and data requirements to power the models that your team is building, and how it can be used to supercharge our ETL pipelines.

Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
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  • You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to dataengineeringpodcast.com/stratacon and dataengineeringpodcast.com/aicon to register today and get 20% off
  • Your host is Tobias Macey and today I’m interviewing Thomas Henson about what data engineers need to know about deep learning, including how to use it for their own projects

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by giving an overview of what deep learning is for anyone who isn’t familiar with it?
  • What has been your personal experience with deep learning and what set you down that path?
  • What is involved in building a data pipeline and production infrastructure for a deep learning product?
    • How does that differ from other types of analytics projects such as data warehousing or traditional ML?
  • For anyone who is in the early stages of a deep learning project, what are some of the edge cases or gotchas that they should be aware of?
  • What are your opinions on the level of involvement/understanding that data engineers should have with the analytical products that are being built with the information we collect and curate?
  • What are some ways that we can use deep learning as part of the data management process?
    • How does that shift the infrastructure requirements for our platforms?
  • Cloud providers have been releasing numerous products to provide deep learning and/or GPUs as a managed platform. What are your thoughts on that layer of the build vs buy decision?
  • What is your litmus test for whether to use deep learning vs explicit ML algorithms or a basic decision tree?
    • Deep learning algorithms are often a black box in terms of how decisions are made, however regulations such as GDPR are introducing requirements to explain how a given decision gets made. How does that factor into determining what approach to take for a given project?
  • For anyone who wants to learn more about deep learning, what are some resources that you recommend?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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