Amazon Web Services (AWS) has released SageMaker, a fully managed end-to-end machine learning service and a video camera that runs deep learning models called DeepLens in an effort to bring machine learning to the enterprise.
The SageMaker machine learning model can run on general instance types or GPU powered instances, beginning with a Jupyter notebook for data exploration, cleaning, and reprocessing of the data.
And enterprises can utilize any of common supervised and unsupervised learning algorithms and frameworks which are built into the product, or create their own model. And the training can scale to tens of instances to support faster model building.
Andy Jassy, Amazon Web Services' CEO at the Re:Invent conference in Las Vegas, stated that builders don’t want machine learning to be so difficult. They don’t want it to be so cryptic. They don’t want it to be black box. They want it to be much easier to engage with.
Simplifying the building of the machine learning models, Jassy said the techniques will be within reach of businesses without the need to employ specialists. Which according to him, there aren’t that many machine learning expert practitioners in the world nowadays.
The DeepLens come loaded with a set of pre-trained machine learning models to give developers ‘hands on experience’ in image detection and recognition. Albeit, developers can also train their own models with SageMaker and run them on the camera.
The $245 high definition camera can capture 1080P video, and sound through a 2D microphone array.
It runs Ubuntu 16.04 and is preloaded with AWS’ Greengrass Core. While there’s also a device-optimised version of MXNet, and other frameworks such as TensorFlow and Caffe2 can also be employed.
DeepLens can run incoming video and audio through on-board deep learning models quickly and with low latency, making use of the cloud for more compute-intensive higher-level processing.