The carmaker begins to open up about its Munich showroom test, but stops short of endorsing lights for data analytics.

Amazon Elastic Container Service for Kubernetes (Amazon EKS) is a managed service that makes it easy for you to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes clusters.

The Amazon Elastic Container Service (ECS) command line interface (CLI) is now available as version 1.1.0.

Global Tables builds upon DynamoDB’s global footprint to provide you with a fully managed, multi-region, and multi-master database that provides fast, local, read and write performance for massively scaled, global applications. Global Tables replicates your Amazon DynamoDB tables automatically across your choice of AWS regions.

Global Tables eliminates the difficult work of replicating data between regions and resolving update conflicts, enabling you to focus on your application’s business logic. In addition, Global Tables enables your applications to stay highly available even in the unlikely event of isolation or degradation of an entire region.

Amazon Aurora Multi-Master allows you to create multiple read/write master instances across multiple Availability Zones. This enables applications to read and write data to multiple database instances in a cluster, just as you can read across Read Replicas today.

Amazon Glacier Select is a new way to query archived data in Amazon Glacier. Glacier Select allows queries to run directly on data stored in Amazon Glacier, retrieving only the data you need out of your archives to use for analytics. This allows you to reduce total cost of ownership while massively extending your data lake into cost-effective archive storage.

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.

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.

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 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.