Amazon has launched a new wireless camera, AWS DeepLens, designed to help train developers in deep learning technologies.
At Amazon Web Services (AWS) conference in Las Vegas last week, the company announced AWS DeepLens, a wireless camera designed to simplify the development of computer vision products.
The idea behind DeepLens is that it will enable developers to design and create AI and machine learning-based products, “in minutes”, using preconfigured frameworks already on the device, according to the company. These frameworks enable the camera to capture and interpret text characters that appear in a video stream and help developers to build image-recognition models. For example, a pet feeder incorporating the technology could recognize individual pets and feed them on demand.
The DeepLens kit comprises a compact Atom X5-powered miniature PC with audio output, two USB 2.0 connections, micro-HDMI, and a micro-SD slot for storage expansion.
Inside, the camera has 8GB of RAM and 16GB of storage. It runs Canonical’s Ubuntu Linux 16.04 Long Term Support (LTS) operating system.
The device has been developed in collaboration with chipmaker Intel. Miles Kingston, general manager of the Smart Home Group at Intel, said that DeepLens “brings together the full range of Intel’s hardware and software expertise to give developers a powerful tool to create new experiences, providing limitless potential for smart home integrations.”
DeepLens will face competition from Google’s AIY Vision Kit, which is considerably cheaper. DeepLens is available for $249 pre-order, with shipments expected in April.
In addition to DeepLens, Amazon also announced its SageMaker service aimed at model building and training for machine learning. It said that this service removes the “heavy lifting and guesswork from each step of the machine learning process.”
The service automatically provisions and manages the infrastructure to both train models and run inference to make predictions using these models. Amazon SageMaker includes ten of the most common deep learning algorithms (for example, k-means clustering, factorisation machines, linear regression, and principal component analysis), which AWS has optimized to run up to ten times faster than standard implementations.
Muck and complexity
Developers simply choose an algorithm and specify their data source, and Amazon SageMaker installs and configures the underlying drivers and frameworks. Amazon SageMaker includes native integration with TensorFlow and Apache MXNet with additional framework support coming soon.
“Our original vision for AWS was to enable any individual in his or her dorm room or garage to have access to the same technology, tools, scale, and cost structure as the largest companies in the world. Our vision for machine learning is no different,” said Swami Sivasubramanian, vice president of machine learning at AWS.
“We want all developers to be able to use machine learning much more expansively and successfully, irrespective of their machine learning skill level. Amazon SageMaker removes a lot of the muck and complexity involved in machine learning to allow developers to easily get started and become competent in building, training, and deploying models.”