As companies welcome more autonomous robots and other heavy equipment into the workplace, we need to ensure equipment can operate safely around human teammates. In this post, we will show you how to build a virtual boundary with computer vision and AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning (ML). Using the machine learning techniques in this post, you can build virtual boundaries for restricted areas that automatically shut down equipment or sound an alert when humans come close. For this project, you will train a custom object detection model with Amazon SageMaker and deploy the model to an AWS DeepLens device. Object detection is an ML algorithm that takes an image as input and identifies objects and their location within the image.

With less than 40 days to re:Invent 2019, the excitement is building up and we are looking forward to seeing you all soon! Continuing our journey on artificial intelligence and machine learning, we are bringing a lot of technical content this year, with over 200 breakout sessions, deep-dive chalk talks, hands-on exercises with workshops featuring Amazon SageMaker, AWS DeepRacer, and deep learning frameworks such as TensorFlow, PyTorch, and more. You'll hear from many customers including Vanguard, BBC, Autodesk, British Airways, Fannie Mae, Thermo Fisher, Intuit, and many more. We are also hosting the Machine Learning Summit again this year, where you will hear from researchers and entrepreneurs about the latest breakthroughs today and the future possibilities tomorrow. To get you started on planning, here are a few highlights for the AI and ML sessions from the re:Invent 2019 session catalog.

Learn the basics of deep learning - a machine learning technique that uses neural networks to learn and make predictions - through computer vision projects, tutorials, and real world, hands-on exploration with a physical device. AWS DeepLens lets you run deep learning models locally on the camera to analyze and take action on what it sees.

We are bringing you four challenges to choose from–sustainability, games, health and inclusivity. Now you can be inspired to create machine learning projects with AWS DeepLens and make a difference at the same time! Use these challenges to gain machine learning experience with fun, collaborative, and inspiring projects. In addition, you'll be making a positive impact on improving people's lives and supporting non-profit organizations that benefit our society. You are invited to enter a single challenge or as many of them as you want.

Amazon's DeepLens, a deep learning enabled video camera, is now generally available and hitting the market for $249. AWS DeepLens is designed to run models via TensorFlow and Caffe in less then 10 minute startup time for developers. The overall effort is to put more machine learning tool into the field and with developers. As for the hardware, DeepLens is a 4 megapixel camera with 1080P video, 2D microphone array, Intel Atom processor and 8GB of memory for models and code. The device runs Ubuntu 16.04, AWS Greengrass Core and optimized versions of MXNet and Intel clDNN libraries.

Nathan Stone (NS) and Peter McLean (PM) are a team both professionally at Haven Power, a business energy supplier in Ipswich, UK, and also off the clock when they recently collaborated to create SafeHaven, the third place winner in the AWS DeepLens Challenge. SafeHaven was designed to protect vulnerable people, by enabling them to identify "who is at the door?" using an Alexa Skill. Unknown visitors trigger SMS or email alerts to relatives or carers, via an SNS subscription. Nathan, a BI Developer and Pete, a Data Architect, have been using AWS services to design and build the BI platform at Haven Power. However, prior to using AWS DeepLens they had no machine learning (ML) experience and didn't know where to begin.

At AWS re:Invent 2017 we announced the AWS DeepLens Challenge in conjunction with Intel. The AWS DeepLens Challenge gave attendees of the re:Invent DeepLens workshops an opportunity to put their skills to the test by building a machine learning (ML) project using their AWS DeepLens. The mission was to get creative with computer vision and deep learning, and learn in the process! All of the contestants put forward amazing entries. We were blown away by the incredible uses they found for AWS DeepLens.

We are excited to launch a new feature for AWS DeepLens that allows you to import models trained using Amazon SageMaker directly into the AWS DeepLens console with one click. This feature is available as of AWS DeepLens software version 1.2.3. You can update your AWS DeepLens software by re-booting your device or by using the command sudo apt-get install awscam on the Ubuntu terminal. For this tutorial, you need the MXNet version 0.12. You can update the MXNet version by using the command sudo pip3 install mxnet 0.12.1.

Last week at the Amazon Web Services' re:Invent conference, AWS and Intel introduced a new video camera, AWS DeepLens, that acts as an intelligent device that can run deep learning algorithms on captured images in real-time. The key difference between DeepLens and any other AI-powered camera lies in the horsepower that makes it possible to run machine learning inference models locally without ever sending the video frames to the cloud. Developers and non-developers rushed to attend the AWS workshop on DeepLens to walk away with a device. There, they were enticed with a hot dog to perform the infamous "Hot Dog OR Not Hot Dog" experiment. I managed to attend one of the repeat sessions, and carefully ferried the device back home.

AWS DeepLens Looking for a new way to learn machine learning? Let a machine teach you with AWS DeepLens, the world's first deep learning enabled video camera for developers. Designed to connect securely to a variety of AWS offerings, including AWS IoT, Amazon SQS, Amazon SNS, and Amazon DynamoDB, AWS DeepLens uses Amazon Kinesis Video Streams to stream video back to AWS and Amazon Rekognition Video to apply advanced video analytics. Easy to customize and fully programmable with AWS Lambda, AWS DeepLens runs on any deep learning framework, including TensorFlow and Caffe.