INSUBCONTINENT EXCLUSIVE:
Back at its re:Invent conference in November, AWS announcedits $249 DeepLens, a camera that specifically geared toward developers who want
to build and prototype vision-centric machine learning models
The company started taking pre-orders for DeepLensa few months ago, but now the camera is actually shipping to developers.
Ahead of today
launch, I had a chance to attend a workshop in Seattle with DeepLenssenior product manager Jyothi Nookula and Amazon VP for AI Swami
Sivasubramanian to get some hands-on time with the hardware and the software services that make it tick.
DeepLens is essentially a small
Ubuntu- and Intel Atom-based computer with a built-in camera that powerful enough to easily run and evaluate visual machine learning models
In total, DeepLens offers about 106 GFLOPS of performance.
The hardware has all of the usual I/O ports (think Micro HDMI, USB 2.0, Audio
out, etc.) to let you create prototype applications, no matter whether those are simple toy apps that send you an alert when the camera
detects a bear in your backyard or an industrial application that keeps an eye on a conveyor belt in your factory
The 4 megapixel camera isn&t going to win any prizes, but it perfectly adequate for most use cases
Unsurprisingly, DeepLens is deeply integrated with the rest of AWS services
Those include the AWS IoT service Greengrass, which you use to deploy models to DeepLens, for example, but also SageMaker, Amazon newest
tool for building machine learning models.
These integrations are also what makes getting started with the camera pretty easy
Indeed, if all you want to do is run one of the pre-built samples that AWS provides, it shouldn&t take you more than 10 minutes to set up
your DeepLens and deploy one of these models to the camera
Those project templates include an object detection model that can distinguish between 20 objects (though it had some issues with toy dogs,
as you can see in the image above), a style transfer example to render the camera image in the style of van Gogh, a face detection model and
a model that can distinguishbetween cats and dogs and one that can recognize about 30 different actions (like playing guitar, for example)
The DeepLens team is also adding a model for tracking head poses
Oh, and there also a hot dog detection model.
But that obviously just the beginning
As the DeepLens team stressed during our workshop, even developers who have never worked with machine learning can take the existing
templates and easily extend them
In part, that due to the fact that a DeepLens project consists of two parts: the model and a Lambda function that runs instances of the
model and lets you perform actions based on the model output
And with SageMaker, AWS now offers a tool that also makes it easy to build models without having to manage the underlying
infrastructure.
You could do a lot of the development on the DeepLens hardware itself, given that it is essentially a small computer, though
you&re probably better off using a more powerful machine and then deploying to DeepLens using the AWS Console
If you really wanted to, you could use DeepLens as a low-powered desktop machine as it comes with Ubuntu 16.04 pre-installed.
For developers
who know their way around machine learning frameworks, DeepLens makes it easy to import models from virtually all the popular tools,
including Caffe, TensorFlow, MXNet and others
It worth noting that the AWS team also built a model optimizer for MXNet models that allows them to run more efficiently on the DeepLens
device.
So why did AWS buildDeepLens &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 said
&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 decide to build its own hardware instead of simply working with a partner &We had a specific customer
experience in mind and wanted to make sure that the end-to-end experience is really easy,& he said
&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
It takes too long for somebody who excited about learning deep learning and building something fun.&
So if you want to get started with deep
learning and build some hands-on projects, DeepLens is now available on Amazon
At $249, it not cheap, but if you are already using AWS — and maybe even use Lambda already — it probably the easiest way to get started
with building these kind of machine learning-powered applications.
