Amazon starts shipping its $249 DeepLens AI camera for developers – TechCrunch

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Again at its re:Invent convention in November, AWS introduced its $249 DeepLens, a digicam that’s particularly geared towards builders who wish to construct and prototype vision-centric machine studying fashions. The corporate began taking pre-orders for DeepLens a couple of months in the past, however now the digicam is definitely delivery to builders.

Forward of immediately’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 and the software program providers that make it tick.

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

The has all the regular I/O ports (suppose Micro HDMI, USB, Audio out, and many others.) to allow you to create prototype purposes, irrespective of whether or not these are easy toy apps that ship you an alert when the digicam detects a bear in your yard or an industrial software that retains an eye fixed on a conveyor belt in your manufacturing unit. The four megapixel digicam isn’t going to win any prizes, nevertheless it’s completely satisfactory for many use instances. Unsurprisingly, DeepLens is deeply built-in with the remainder of AWS’s providers. These embrace the AWS IoT service Greengrass, which you employ to deploy fashions to DeepLens, for instance, but additionally SageMaker, Amazon’s latest device for constructing machine studying fashions.

These integrations are additionally what makes getting began with the digicam fairly straightforward. Certainly, if all you wish to do is run one of many pre-built samples that AWS supplies, it shouldn’t take you greater than 10 minutes to arrange your DeepLens and deploy considered one of these fashions to the digicam. These challenge templates embrace an object detection mannequin that may distinguish between 20 objects (although it had some points with toy canines, as you possibly can see within the picture above), a method switch instance to render the digicam 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 taking part in guitar, for instance). The DeepLens crew can also be including a mannequin for monitoring head poses. Oh, and there’s additionally a sizzling canine detection mannequin.

However that’s clearly only the start. Because the DeepLens crew pressured throughout our workshop, even builders who’ve by no means labored with machine studying can take the prevailing templates and simply prolong them. Partially, that’s on account of the truth that a DeepLens challenge consists of two components: the mannequin and a Lambda operate 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 provides a device that additionally makes it straightforward to construct fashions with out having to handle the underlying infrastructure.

You would do a number of the event on the DeepLens itself, on condition 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. For those who actually needed to, you may 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 straightforward to import fashions from nearly all the favored instruments, together with Caffe, TensorFlow, MXNet and others. It’s price noting that the AWS crew additionally constructed a mannequin optimizer for MXNet fashions that enables them to run extra effectively on the DeepLens gadget.

So why did AWS construct DeepLens? “The entire rationale behind DeepLens got here from a easy query that we requested ourselves: How will we put machine studying within the arms of each developer,” Sivasubramanian mentioned. “To that finish, we brainstormed plenty of concepts and probably the most promising thought was really that builders like to construct options as hands-on style on units.” And why did AWS resolve to construct its personal as a substitute of merely working with a companion? “We had a selected buyer expertise in thoughts and needed to guarantee that the end-to-end expertise is very easy,” he mentioned. “So as a substitute of telling someone to go obtain this toolkit after which go purchase this toolkit from Amazon after which wire all of those collectively. […] So it’s a must to do like 20 various things, which usually takes two or three days after which it’s a must to put your complete infrastructure collectively. It takes too lengthy for someone who’s enthusiastic about studying deep studying and constructing one thing enjoyable.”

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 best technique to get began with constructing these type of machine learning-powered purposes.

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