ABD203: Real-Time Streaming Applications on AWS: Use Cases and Patterns

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.

Data speaks. Discover how Ivy Tech, the nation's largest singly accredited community college, uses AWS to gather, analyze, and take action on student behavioral data for the betterment of over 3,100 students. This session outlines the process from inception to implementation across the state of Indiana and highlights how Ivy Tech's model can be applied to your own complex business problems.

Auto Scaling allows cloud resources to scale automatically in reaction to the dynamic needs of customers, which helps to improve application availability and reduce costs. New target tracking scaling policies for Auto Scaling make it easy to set up dynamic scaling for your application in just a few steps. With target tracking, you simply select a load metric for your application, set the target value, and Auto Scaling adjusts resources as needed to maintain that target. In this session, you will learn how you can use target tracking to setup sound scaling policies “without the fuss”, and improve availability under fluctuating demand. Netflix is spending $6 billion on original content this year, with shows like The Crown, Narcos, and Stranger Things, and plenty more in the future. Hear how they're using target tracking scaling policies to improve performance, reliability and availability around the world at prime times, without over-provisioning - and without guesswork. They will share some best practices examples of how target tracking allows their infrastructure to automatically adapt to changing traffic patterns in order to keep their audience entertained and their costs on target.

With customers demanding relevant and real-time experiences across a range of devices, digital businesses are looking to gather user data at scale, understand this data, and respond to customer needs instantly. This requires tools that can record large volumes of user data in a structured fashion, and then instantly make this data available to generate insights. In this session, we demonstrate how you can use Amazon Pinpoint to capture user data in a structured yet flexible manner. Further, we demonstrate how this data can be set up for instant consumption using services like Amazon Kinesis Firehose and Amazon Redshift. We walk through example data based on real world scenarios, to illustrate how Amazon Pinpoint lets you easily organize millions of events, record them in real-time, and store them for further analysis.

Batch processing is useful to analyze large amounts of data. But configuring and scaling a cluster of virtual machines to process complex batch jobs can be difficult. In this talk, we'll show how to use containers on AWS for batch processing jobs that can scale quickly and cost-effectively. We will also discuss AWS Batch, our fully managed batch-processing service. You'll also hear from GoPro and Here about how they use AWS to run batch processing jobs at scale including best practices for ensuring efficient scheduling, fine-grained monitoring, compute resource automatic scaling, and security for your batch jobs.

Motion detection triggers have reduced the amount of video recorded by modern devices. But maybe you want to reduce that further—maybe you only care if a car or a person is on-camera before recording or sending a notification. Security cameras and smart doorbells can use Amazon Rekognition to reduce the number of false alarms. Learn how device makers and home enthusiasts are building their own smart layers of person and car detection to reduce false alarms and limit video volume. Learn too how you can use face detection and recognition to notify you when a friend has arrived.

Analyzing and debugging production distributed applications built using a service oriented, microservices, or serverless architectures is a challenging task. In this session, we introduce AWS X-Ray, an AWS service that makes it easier to identify performance bottlenecks and errors, pinpoint issues to specific services in your application, identify the impact of issues on application users, and visualize the service call graph and the request timelines for your applications. We will also showcase a customer, Chick-fil-A and how they have adopted AWS X-Ray to play a role throughout the microservice lifecycle in order to ensure quality, transparency, and operational visibility for their services on AWS

We're still in the midst of a data explosion. With data, we want to do things smarter, quicker, and more accurately, and working with a company like Snowflake enables us to do so. In this session, Kelly Mungary, Lionsgate director of enterprise data and analytics, details how Lionsgate achieved the “Holy Grail” of data analytics – easily storing, blending, and analyzing structured and semi-structured data for agile reporting and data science. With modern data warehousing built for the cloud, Lionsgate achieves faster time to market and continues to migrate investment away from traditional, fading platforms. Lionsgate is a leading global entertainment company that generates vast stores of varied data types from sales, research, social media, exit polls, box office activity, and many other channels to better serve its business customers and its fans. Session sponsored by Snowflake