Practical Artificial Intelligence in the Cloud by Mike Barlow

Author:Mike Barlow
Language: eng
Format: epub, mobi, pdf
Publisher: O'Reilly Media, Inc.
Published: 2016-10-19T04:00:00+00:00

Building in the Cloud

Lucini foresees AI in the cloud penetrating multiple sectors of the broader economy. “I think all industries are going to take advantage of this. If you’re crunching through huge amounts of data, the cloud is the only way to go,” he says. “Why buy 1,000 machines to do a job when you can rent the machines in the cloud for a couple of weeks?”

David Laxer, a data scientist and software developer, says it’s hard to justify purchasing “custom hardware that will be obsolete in a year or two” when you can rent or lease the resources you need in the cloud.

“Let’s say I’m working on a semantic hashing algorithm and my document collection is huge—say, the size of the U.S. Patent Office database. I can’t do that on my Mac. I have to do that in the cloud,” says Laxer. “I can upload my data to an EC2 (Amazon Elastic Compute Cloud) instance and start training my models with deep learning using Spark, do the testing, and actually deploy an application.”

Additionally, developers can choose among several options for renting compute resources in the cloud. “With EC2, for example, you can get a reserved arrangement where particular servers are yours for a month or for as long you need them. Or you can go a cheaper route and bid on what Amazon calls ‘Spot instances.’ The downside of bidding is that if someone outbids you, then you lose the instance. It’s like buying a reserved seat at a ballpark versus buying a seat in the bleachers,” he explains.

From the perspective of freelance AI developers, the cloud offers the best deal. “You can’t do this in your garage. The cost of buying servers would be prohibitive,” he says.

However, it can take a while for some advances in technology to fully penetrate the cloud. Not every cloud provider offers GPU instances, says Laxer, and some of the available GPU instances are several years old.

In some cases, it might make more sense to buy a high-performance board and install it in a heavy-duty workstation—assuming you have air conditioning or water cooling. But you can only put four boards into a server unless you’ve got a rack mount, and you’re probably not going to do that in your garage, he says.

“If you’re Google or Facebook, you’ve got buildings full of servers with GPUs,” says Laxer. “If you’re a freelance developer and you’re training deep belief networks or doing reinforcement learning, you won’t be able to solve AlphaGo-class problems”.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.