AWS Announces a New IoT and Machine Learning Services, Deep Learning-Enabled Video Camera
Amazon Web Services annual re:Invent conference kicked off today in Las Vegas and AWS announced the DeepLens deep learning-enabled wireless video camera, five new machine learning services and a slew of new IoT services.
Adoption of open cloud technology
We are starting with Amazon.com Inc's announcement that it is adopting Kubernetes, a popular open-source technology.
Kubernetes has emerged as a standard among companies as they build more applications on public clouds, the big computer data centers that are displacing traditional customer-owned computer systems.
Earlier this year companies including Microsoft, Oracle and IBM announced their support for Kubernetes, which was originally developed by a team at Google.
One of Kubernetes' key advantages is its ability to run an application on any public cloud, including Microsoft's Azure and Google Cloud Platform, making it easier to migrate from one cloud vendor to another.
DeepLens, a Deep Learning-Enabled Wireless Video Camera for developers
AWS DeepLens is a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning.
AWS and Intel collaborated on the DeepLens camera to provide builders of all skill levels with tools needed to design and create artificial intelligence (AI) and machine learning products.
DeepLens combines high amounts of processing power with an easy-to-learn user interface to support the training and deployment of models in the cloud. Powered by an Intel Atom X5 processor with embedded graphics that support object detection and recognition, DeepLens uses Intel-optimized deep learning software tools and libraries (including the Intel Math Kernel Library) to run real-time computer vision models directly on the device for reduced cost and real-time responsiveness.
Developers can start designing and creating AI and machine learning products in a matter of minutes using the preconfigured frameworks already on the device. Apache MXNe is supported today, and Tensorflow and Caffe2 will be supported in 2018's first quarter.
Amazon SageMaker is a fully managed service that removes the heavy lifting and guesswork from each step of the machine learning process. It provides pre-built development notebooks, popular machine learning algorithms optimized for petabyte-scale datasets, and automatic model tuning. Amazon SageMaker also simplifies and accelerates the training process, automatically provisioning and managing the infrastructure to both train models and run inference to make predictions using these models.
With Amazon SageMaker developers can:
- Build machine learning models with performance-optimized algorithms: Amazon SageMaker is a fully managed machine learning notebook environment makes it easy for developers to explore and visualize data they have stored in Amazon Simple Storage Service (Amazon S3), and transform it using all of the popular libraries, frameworks, and interfaces. Amazon SageMaker includes ten of the most common deep learning algorithms (e.g. k-means clustering, factorization machines, linear regression, and principal component analysis), which AWS has optimized to run up to ten times faster than standard implementations.
- Developers 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. Developers can also specify any framework and algorithm they choose by uploading them into a container on the Amazon EC2 Container Registry.
- Amazon SageMaker makes training easy. Developers select the type and quantity of Amazon EC2 instances and specify the location of their data. Amazon SageMaker sets up the distributed compute cluster, performs the training, outputs the result to Amazon S3, and tears down the cluster when complete. Amazon SageMaker can automatically tune models with hyper-parameter optimization, adjusting thousands of different combinations of algorithm parameters to arrive at the most accurate predictions.
- Deploy models into production with one click: Amazon SageMaker takes care of launching instances, deploying the model, and setting up a secure HTTPS end-point for the application to achieve high throughput and low latency predictions, as well as auto-scaling Amazon EC2 instances across multiple availability zones (AZs). It also provides native support for A/B testing. Once in production, Amazon SageMaker eliminates the heavy lifting involved in managing machine learning infrastructure, performing health checks, applying security patches, and conducting other routine maintenance.
New speech, language, and vision services
For those developers who are not experts in machine learning, but are interested in using these technologies to build a new class of apps that exhibit human-like intelligence, Amazon Transcribe, Amazon Translate, Amazon Comprehend, and Amazon Rekognition video provide machine learning services that are scalable and cost-effective.
- Amazon Transcribe (available in preview) converts speech to text, allowing developers to turn audio files stored in Amazon S3 into accurate, fully punctuated text. Amazon Transcribe has been trained to handle even low fidelity audio, such as contact center recordings, with a high degree of accuracy. Amazon Transcribe can generate a time stamp for every word so that developers can precisely align the text with the source file. Today, Amazon Transcribe supports English and Spanish with more languages to follow. In the coming months, Amazon Transcribe will have the ability to recognize multiple speakers in an audio file, and will also allow developers to upload custom vocabulary for more accurate transcription for those words.
- Amazon Translate (available in preview) uses state of the art neural machine translation techniques to provide accurate translation of text from one language to another. Amazon Translate can translate short or long-form text and supports translation between English and six other languages (Arabic, French, German, Portuguese, Simplified Chinese, and Spanish), with many more to come in 2018.
- Amazon Comprehend (available today) can understand natural language text from documents, social network posts, articles, or any other textual data stored in AWS. Amazon Comprehend uses deep learning techniques to identify text entities (e.g. people, places, dates, organizations), the language the text is written in, the sentiment expressed in the text, and key phrases with concepts and adjectives, such as 'beautiful' or 'sunny.' Amazon Comprehend has been trained on a wide range of datasets, including product descriptions and customer reviews from Amazon.com, to build language models that extract key insights from text. It also has a topic modeling capability that helps applications extract common topics from a corpus of documents. Amazon Comprehend integrates with AWS Glue to enable end-to-end analytics of text data stored in Amazon S3, Amazon Redshift, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, or other popular Amazon data sources.
- Amazon Rekognition Video (available today) can track people, detect activities, and recognize objects, faces, celebrities, and inappropriate content in millions of videos stored in Amazon S3. It also provides real-time facial recognition across millions of faces for live stream videos. Amazon Rekognition Video's API is powered by computer vision models that are trained to accurately detect thousands of objects and activities, and extract motion-based context from both live video streams and video content stored in Amazon S3. Amazon Rekognition Video can automatically tag specific sections of video with labels and locations (e.g. beach, sun, child), detect activities (e.g. running, jumping, swimming), detect, recognize, and analyze faces, and track multiple people, even if they are partially hidden from view in the video.
New IoT Services
Amazon Web Services also today announced six services and capabilities for connected devices at the edge. AWS IoT 1-Click, AWS IoT Device Management, AWS IoT Device Defender, AWS IoT Analytics, Amazon FreeRTOS, and AWS Greengrass ML Inference make getting started with IoT as easy as one click.
- AWS IoT 1-Click: get started with AWS IoT (available in preview). When considering IoT, many just want an easy way to get started by enabling devices to perform simple functions. These are functions like single-button devices that call technical support, reorder goods and services, or track asset locations. With AWS IoT 1-Click, enabling a device with an AWS Lambda function is as easy as downloading the mobile app, registering and selecting an AWS IoT 1-Click enabled device, and - with a single click - associating an AWS Lambda function. AWS IoT 1-Click comes with pre-built AWS Lambda code for common actions like sending an SMS or email.
- AWS IoT Device Management (available today) makes it easy to securely onboard, organize, monitor, and remotely manage IoT devices at scale throughout their lifecycle-from initial setup, through software updates, to retirement. Getting started is easy; AWS customers simply log into the AWS IoT Console to register devices, individually or in bulk, and then upload attributes, certificates, and access policies. Once devices are in service, AWS IoT Device Management allows customers to group and track devices, find any device in near real-time, troubleshoot device functionality, remotely update device software, and remotely reboot, reset, patch, and restore devices to factory settings, reducing the cost and effort of managing large IoT device deployments.
- AWS IoT Device Defender (coming in the first half of 2018) continuously audits security policies associated with devices to make sure that they aren't deviating from security best practices, and alerting AWS customers when non-compliant devices are detected. AWS IoT Device Defender also monitors the activities of fleets of devices.
- AWS IoT Analytics (available in preview) is a fully managed analytics service that cleans, processes, stores, and analyzes IoT device data at scale. Getting started is easy: AWS customers identify the device data they wish to analyze, and they can optionally choose to enrich the device data with IoT-specific metadata, such as device type and location, by using the AWS IoT Device Registry and other public data sources. AWS IoT Analytics also has features for more sophisticated analytics, like statistical inference.
- New AWS Greengrass feature brings machine learning to the edge (available in preview) . AWS Greengrass ML Inference is a new feature of AWS Greengrass that lets application developers add machine learning to their devices, without requiring special machine learning skills. IoT devices frequently collect and forward large quantities of data, which can be used to automate real-time decision making through machine learning. To do this, AWS customers build, train, and run machine learning on their IoT data in the cloud. However, some applications are highly latency sensitive and require the ability to make decisions without relying on always-on network connectivity. With AWS Greengrass ML Inference, devices can run machine learning models to perform inference locally, get results, and then make smart decisions quickly, even when they're not connected.
Amazon FreeRTOS allows connections of small, low-power devices to the cloud
Today, devices are already capable of connecting to the cloud. Many of these devices contain enough onboard computing power (CPU) to take advantage of AWS IoT services. However, a large number of other devices-from lightbulbs and conveyer belts to motion detectors' aren't big enough to house a CPU and possess a microcontroller (MCU) instead. The most popular operating system used for these devices is FreeRTOS, an open source operating system for microcontrollers that allows them to perform simple tasks.
Amazon FreeRTOS extends FreeRTOS with software libraries that make it easy to securely connect small, low-power devices to AWS cloud services like AWS IoT Core, or to more powerful edge devices and gateways running AWS Greengrass (a software module that resides inside devices and gives customers the same Lambda programming model as exists within the AWS Cloud). With Amazon FreeRTOS, developers can build devices with common IoT capabilities, including networking, over-the-air software updates, encryption, and certificate handling. Developers can use the Amazon FreeRTOS console to configure and download Amazon FreeRTOS, or go to FreeRTOS.org or GitHub.