Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Deploying deep learning in the cloud

Alex Jaimes (Dataminr)
11:55am–12:35pm Tuesday, May 1, 2018
Location: Concourse A
Average rating: ****.
(4.00, 2 ratings)

What you'll learn

  • Learn how to deploy deep learning in the cloud


Alex Jaimes explains how the cloud can be used effectively to deploy deep learning and the factors that allow you to do so cost effectively. For example, in many situations, the distributed nature of cloud infrastructure can be leveraged to scale Ml/DL applications using CPUs. Along the way, Alex shares examples of when and how to deploy deep learning in the cloud as well as the corresponding benefits, challenges, and opportunities.

Photo of Alex Jaimes

Alex Jaimes


Alejandro (Alex) Jaimes is senior vice president of AI and data science at Dataminr. His work focuses on mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. Alex is a scientist and innovator with 15+ years of international experience in research leading to product impact at companies including Yahoo, KAIST, Telefónica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs. Previously, Alex was head of R&D at DigitalOcean, CTO at AiCure, and director of research and video products at Yahoo, where he managed teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. He was also a visiting professor at KAIST. He has published widely in top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc.) and is a frequent speaker at international academic and industry events. He holds a PhD from Columbia University.

Comments on this page are now closed.


Picture of Alex Jaimes
04/30/2018 6:02pm EDT

It’s a high level overview- there seems to be a lot of confusion in general, for example, in assuming that DL is the solution to every problem, and that GPUs are always needed.

04/30/2018 5:59pm EDT

Will this be about model serving as well as training in the cloud?