The new (2019) edition of the AWS DeepLens can now be purchased in six countries (US, UK, Germany, France, Spain, Italy, and Canada), and preordered in Japan. The 2019 edition is easier to set up, and (thanks to Amazon SageMaker Neo) runs machine learning models up to twice as fast as the earlier edition.
We are also launch a pair of new tutorials to help you to get started:
aws-deeplens-coffee-leaderboard – This tutorial focuses on a demo that uses face detection to track the number of people that drink coffee. It watches a scene, and triggers a Lambda function when a face is detected. Amazon Rekognition is used to detect the presence of a coffee mug, and the face is added to a DynamoDB database that is maintained by (and private to) the demo. The demo also includes a leaderboard that tracks the number of coffees over time. Here’s the architecture:
And here’s the leaderboard:
To learn more, read Track the number of coffees consumed using AWS DeepLens.
aws-deeplens-worker-safety-project – This tutorial focuses on a demo that identifies workers that are not wearing safety helmets. The DeepLens detects faces, and uploads the images to S3 for further processing. The results are analyze using AWS IoT and Amazon CloudWatch, and are displayed on a web dashboard. Here’s the architecture:
To learn more, register for and then take the free 30-minute course: Worker Safety Project with AWS DeepLens.
Detecting Cats, and Cats with Rats
Finally, I would like to share a really cool video featuring my colleague Ben Hamm. After growing tired of cleaning up the remains of rats and other creatures that his cat Metric had killed, Ben decided to put his DeepLens to work. Using a hand-labeled training set, Ben created a model that could tell when Metric was carrying an unsavory item its his mouth, and then lock him out. Ben presented his project at Ignite Seattle and the video has been very popular. Take a look for yourself:
Order Your DeepLens Today
If you are in one of the countries that I listed above, you can order your DeepLens today and get started with Machine Learning in no time flat! Visit the DeepLens home page to learn more.
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