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.
strongDM enables you to easily manage and audit access to databases and servers. Leading organizations including Hearst, SoFi, and Peloton rely on strongDM to eliminate the manual-heavy work required to onboard, offboard, and audit staff’s access to everything. Simplify your access control strategy today and schedule a demo to see how much easier your life can be.
Do you want to try out some of the tools and applications that you heard about on the Data Engineering Podcast? Do you have some ETL jobs that need somewhere to run? Check out Linode at dataengineeringpodcast.com/linode or use the code dataengineering2019 and get a $20 credit (that’s 4 months free!) to try out their fast and reliable Linux virtual servers. They’ve got lightning fast networking and SSD servers with plenty of power and storage to run whatever you want to experiment on.
- 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!
- Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems.
- Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
- To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
- Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Dell EMC
- DBA (Database Administrator)
- Deep Learning
- Machine Learning
- Neural Networks
- Feature Engineering
- SVD (Singular Value Decomposition)
- Andrew Ng
- Unstructured Data Solutions Team of Dell EMC
- GPU (Graphics Processing Unit)
- Nvidia RAPIDS
- Project Hydrogen
- ETL (Extract, Transform, Load)
- Supervised Learning
- Unsupervised Learning
- Apache Kudu
- CNN (Convolutional Neural Network)
- Sentiment Analysis
- Weapons Of Math Destruction by Cathy O’Neil
- Deep Learning Bootcamps
- Thomas Henson Tensorflow Course on Pluralsight
- Google ML Bootcamp
- Caffe deep learning framework