Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Apache TinkerPop and W3C’s RDF, and their associated query languages TinkerPop Gremlin and RDF SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

Customers use Amazon Kinesis to run real-time analytics using data streams. Today, we are adding video streams to Kinesis. We are announcing Amazon Kinesis Video Streams, a fully managed video ingestion and storage service. Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for machine learning, analytics, and processing. You can also stream other time-encoded data like RADAR and LIDAR signals using Kinesis Video Streams.

Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.

Build
Amazon SageMaker makes it easy to build ML models and get them ready for training by providing everything you need to quickly connect to your training data, and to select and optimize the best algorithm and framework for your application. Amazon SageMaker includes hosted Jupyter notebooks that make it is easy to explore and visualize your training data stored in Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook.

To help you select your algorithm, Amazon SageMaker includes the 10 most common machine learning algorithms which have been pre-installed and optimized to deliver up to 10 times the performance you’ll find running these algorithms anywhere else. Amazon SageMaker also comes pre-configured to run TensorFlow and Apache MXNet, two of the most popular open source frameworks. You also have the option of using your own framework.

Train
You can begin training your model with a single click in the Amazon SageMaker console. Amazon SageMaker manages all of the underlying infrastructure for you and can easily scale to train models at petabyte scale. To make the training process even faster and easier, AmazonSageMaker can automatically tune your model to achieve the highest possible accuracy.

Deploy
Once your model is trained and tuned, Amazon SageMaker makes it easy to deploy in production so you can start running generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. Amazon SageMaker also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results.

Amazon SageMaker takes away the heavy lifting of machine learning, so you can build, train, and deploy machine learning models quickly and easily.

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation uses deep learning to deliver more accurate and more natural sounding translation than older statistical and rule-based translation algorithms. Amazon Translate enables translation at scale so that you can easily translate large volumes of text efficiently to handle tasks like localizing content for international users and facilitating real-time cross-lingual communication.

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.