AWS re:Invent 2017 AWS

Join us to hear about our strategy for driving machine learning innovation for our customers and learn what's new from AWS in the machine learning space. Swami Sivasubramanian, VP of Amazon Machine Learning, will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.

AWS has launched Amazon Sumerian. Sumerian lets you create and run virtual reality (VR), augmented reality (AR), and 3D applications quickly and easily without requiring any specialized programming or 3D graphics expertise. In this session, we will introduce you to Sumerian, and how you can build highly immersive and interactive scenes for the enterprise that run on popular hardware such as Oculus Rift, HTC Vive, and iOS mobile devices.

AWS has launched Amazon Sumerian. Sumerian lets you create and run virtual reality (VR), augmented reality (AR), and 3D applications quickly and easily without requiring any specialized programming or 3D graphics expertise. In this session, we will dive deep into details about Sumerian so you can see what's under the hood. We will cover creating a project, using the visual state machine, connecting an Amazon Sumerian scene to AWS services, and using a Sumerian Host to add presence to your applications.

In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.

Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.

To win in the marketplace and provide differentiated customer experiences, businesses need to be able to use live data in real time to facilitate fast decision making. In this session, you learn common streaming data processing use cases and architectures. First, we give an overview of streaming data and AWS streaming data capabilities. Next, we look at a few customer examples and their real-time streaming applications. Finally, we walk through common architectures and design patterns of top streaming data use cases.