Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to analyze your text. Amazon Comprehend identifies the language of the text, extracts key phrases, places, people, brands, or events, understands sentiment about products or services, and identifies the main topics from a library of documents. The service learns from a variety of information sources, including Amazon.com product descriptions and consumer reviews and it retrains against new data constantly to keep pace with the evolution of language. Because of the depth and breadth of this training approach, it is able to provide accurate coverage for a wide range of scenarios like analyzing customer feedback, intelligent document search, and automatically organizing content.

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech to text capability to their applications. Amazon Transcribe can analyze audio files stored in Amazon S3 and return a text file of the transcribed speech with time stamps for every word so you can easily locate the audio in the original source by searching for the text.

Starting today, AWS IoT Core is providing new authentication mechanisms that allow you to connect your devices to AWS. Using the custom authentication feature, customers can utilize bearer token authentication strategies, such as OAuth, to connect to AWS without using a X.509 certificate on their devices. With this, customers can reuse their existing authentication mechanism that they have already invested in.

AWS Greengrass Machine Learning (ML) Inference makes it easy to perform ML inference locally on AWS Greengrass devices using models that are built and trained in the cloud.  Until now, building and training ML models and running ML inference was done almost exclusively in the cloud.  Training ML models requires massive computing resources so it is a natural fit for the cloud.  With AWS Greengrass ML Inference your AWS Greengrass devices can make smart decisions quickly as data is being generated, even when they are disconnected.

The capability simplifies each step of deploying ML, including accessing ML models, deploying models to devices, building and deploying ML frameworks, creating inference apps, and utilizing on device accelerators such as GPUs and FPGAs.  For example, you can access a deep learning model built and trained in Amazon SageMaker directly from the AWS Greengrass console and then download it to your device as part of an AWS Greengrass group.  AWS Greengrass ML Inference includes a prebuilt Apache MXNet framework to install on AWS Greengrass devices so you don’t have to create this from scratch.  The pre-built Apache MXNet package for NVIDIA Jetson, Intel Apollo Lake, and Raspberry Pi devices can be downloaded directly from the cloud or can be included as part of the software in your AWS Greengrass group.

AWS Greengrass ML Inference also includes prebuilt AWS Lambda templates that you use to create an inference app quickly.  The provided Lambda blueprint shows you common tasks such as loading models, importing Apache MXNet, and taking actions based on predictions.

In many applications, your ML model will perform better when you fully utilize all the hardware resources available on the device, and AWS Greengrass ML Inference helps with this. To let your application access the hardware resources on your device, you declare them as a local resource in your AWS Greengrass group in the AWS Greengrass console.

To use these features, please sign up for the preview.

You can now use AWS IoT Analytics to cleanse, process, enrich, store, and analyze IoT data at scale. It is the easiest way to run analytics on IoT data and get insights that help you make better and more accurate decisions for IoT applications and machine learning use cases.  

AWS is pleased to announce AWS IoT Device Defender, a fully managed service that allows you to secure your fleet of IoT devices on an ongoing basis. AWS IoT Device Defender audits your fleet to ensure it adheres to security best practices, detects abnormal device behavior, alerts you to security issues, and recommends mitigation actions for these security issues. AWS IoT Device Defender is current not generally available. In order to learn more about AWS IoT Device Defender and to express your interest in the service, please sign up here.  

AWS DeepLens is a deep learning enabled wireless video camera specifically designed to help developers of all skill levels grow their machine learning skills through hands-on computer vision tutorials, example code, and pre-built models. DeepLens includes an HD video camera with on-board compute optimized for deep learning, which is fully programmable using AWS Lambda. Developers are free to focus on their machine learning skills, while DeepLens automatically optimizes and deploys models to the device and connects to the cloud for more advanced capabilities. DeepLens integrates with Amazon SageMaker giving developers an end-to-end solution to learn, develop, and test machine learning applications both in the cloud and at the edge, for the very first time.

Amazon FreeRTOS is an IoT operating system for microcontrollers that makes small, low-powered edge devices easy to program, deploy, secure, connect, and maintain. Amazon FreeRTOS extends the FreeRTOS kernel, a popular open source real-time operating system for microcontrollers, and includes software libraries for security, connectivity, and updateability. Amazon FreeRTOS provides everything you need to easily program connected microcontroller-based devices and collect data from them for IoT applications, and helps you scale those applications across millions of devices. Amazon FreeRTOS is free of charge, open source, and available to all today. To learn more and get stared, click here.

Amazon EC2 now allows peering relationships to be established between Virtual Private Clouds (VPCs) across different AWS regions. Inter-Region VPC Peering allows VPC resources like EC2 instances, RDS databases and Lambda functions running in different AWS regions to communicate with each other using private IP addresses, without requiring gateways, VPN connections or separate network appliances.

AWS Systems Manager provides a unified user interface that allows you to view operational data from multiple AWS services and automate operational tasks across your AWS resources.