Home / Tech News / Amazon starts shipping its $249 DeepLens AI camera for developers

Amazon starts shipping its $249 DeepLens AI camera for developers

Back at its re:Invent convention in November, AWS announced its $249 DeepLens, a digital camera that’s particularly geared toward developers who need to construct and prototype vision-centric machine studying fashions. The firm began taking pre-orders for DeepLens a number of months in the past, however now the digital camera is definitely delivery to builders.

Ahead of at this time’s launch, I had an opportunity to attend a workshop in Seattle with DeepLens senior product supervisor Jyothi Nookula and Amazon’s VP for AI Swami Sivasubramanian to get some hands-on time with the {hardware} and the software program providers that make it tick.

DeepLens is actually a small Ubuntu- and Intel Atom-based laptop with a built-in digital camera that’s highly effective sufficient to simply run and consider visible machine studying fashions. In complete, DeepLens affords about 106 GFLOPS of efficiency.

The {hardware} has all the common I/O ports (assume Micro HDMI, USB 2.0, Audio out, and so on.) to allow you to create prototype purposes, irrespective of whether or not these are easy toy apps that ship you an alert when the digital camera detects a bear in your yard or an industrial software that retains an eye fixed on a conveyor belt in your manufacturing facility. The four megapixel digital camera isn’t going to win any prizes, but it surely’s completely ample for many use circumstances. Unsurprisingly, DeepLens is deeply built-in with the remainder of AWS’s providers. Those embrace the AWS IoT service Greengrass, which you utilize to deploy fashions to DeepLens, for instance, but in addition SageMaker, Amazon’s latest software for constructing machine studying fashions.

These integrations are additionally what makes getting began with the digital camera fairly simple. Indeed, if all you need to do is run one of many pre-built samples that AWS gives, it shouldn’t take you greater than 10 minutes to arrange your DeepLens and deploy one in every of these fashions to the digital camera. Those mission templates embrace an object detection mannequin that may distinguish between 20 objects (although it had some points with toy canines, as you may see within the picture above), a method switch instance to render the digital camera picture within the type of van Gogh, a face detection mannequin and a mannequin that may distinguish between cats and canines and one that may acknowledge about 30 totally different actions (like enjoying guitar, for instance). The DeepLens workforce can also be including a mannequin for monitoring head poses. Oh, and there’s additionally a hot dog detection model.

But that’s clearly only the start. As the DeepLens workforce harassed throughout our workshop, even builders who’ve by no means labored with machine studying can take the prevailing templates and simply prolong them. In half, that’s attributable to the truth that a DeepLens mission consists of two elements: the mannequin and a Lambda perform that runs situations of the mannequin and allows you to carry out actions primarily based on the mannequin’s output. And with SageMaker, AWS now affords a software that additionally makes it simple to construct fashions with out having to handle the underlying infrastructure.

You may do a number of the event on the DeepLens {hardware} itself, provided that it’s primarily a small laptop, although you’re most likely higher off utilizing a extra highly effective machine after which deploying to DeepLens utilizing the AWS Console. If you actually needed to, you might use DeepLens as a low-powered desktop machine because it comes with Ubuntu 16.04 pre-installed.

For builders who know their method round machine studying frameworks, DeepLens makes it simple to import fashions from just about all the favored instruments, together with Caffe, TensorFlow, MXNet and others. It’s value noting that the AWS workforce additionally constructed a mannequin optimizer for MXNet fashions that permits them to run extra effectively on the DeepLens machine.

So why did AWS construct DeepLens? “The whole rationale behind DeepLens came from a simple question that we asked ourselves: How do we put machine learning in the hands of every developer,” Sivasubramanian mentioned. “To that end, we brainstormed a number of ideas and the most promising idea was actually that developers love to build solutions as hands-on fashion on devices.” And why did AWS determine to construct its personal {hardware} as an alternative of merely working with a accomplice? “We had a specific customer experience in mind and wanted to make sure that the end-to-end experience is really easy,” he mentioned. “So instead of telling somebody to go download this toolkit and then go buy this toolkit from Amazon and then wire all of these together. […] So you have to do like 20 different things, which typically takes two or three days and then you have to put the entire infrastructure together. It takes too long for somebody who’s excited about learning deep learning and building something fun.”

So if you wish to get began with deep studying and construct some hands-on tasks, DeepLens is now out there on Amazon. At $249, it’s not low-cost, however in case you are already utilizing AWS — and possibly even use Lambda already — it’s most likely the simplest option to get began with constructing these form of machine learning-powered purposes.

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