AWS re:Invent 2018 – Overview and Experience

In this session, get hands-on experience with live industrial devices, and learn how to deploy the AWS Industrial IoT reference solution to consume and analyze factory data from your operational technology (OT). You deploy the reference solution in your AWS account and configure it to connect PLCs from common manufacturers, such as Siemens, Rockwell, and ABB, to AWS IoT, and you send data to the cloud. Gain experience using AWS Greengrass for edge computing, and AWS IoT Analytics and Amazon QuickSight to process and visualize the data for familiar use cases, including predictive maintenance and plant performance optimization.

Red teamers, penetration testers, and attackers can leverage the same tools used by developers to attack AWS accounts. In this session, two technical security experts demonstrate how an attacker can perform reconnaissance and pivoting on AWS, leverage network, AWS Lambda functions, and implementation weaknesses to steal credentials and data. They then show you how to defend your environment from these threats.

Organizations need to gain insight and knowledge from a growing number of IoT, APIs, clickstreams, and unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, we cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL pipelines for your data lake. We also discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. 

In this session, we discuss how customers can use Amazon FreeRTOS on microcontrollers with AWS Greengrass at the edge. We walk through connecting your devices running Amazon FreeRTOS and connecting devices to AWS Greengrass. We also show you how these two services can work together to solve customer use cases. In addition, we cover security best practices for key management and TLS implementation across Amazon FreeRTOS and AWS Greengrass.

In this workshop, cloud architects, IT service management (ITSM) leads, and IT managers learn how to launch and operate governed cloud workloads on AWS by leveraging AWS management tools on the ServiceNow platform. Attendees have the option of managing the provisioning process for an Amazon WorkSpaces desktop through ServiceNow. This workshop simulates the launching of AWS services using the AWS Service Catalog Connector for ServiceNow and the operational activities established to demonstrate the ITSM process integration within ServiceNow (workflows, CMDB updates, incident management, change management).

In this session, we start by deploying a monolithic web app into an Amazon Lightsail instance. Next, we separate out the components and use technologies like load balancers and snapshots to show how to deploy your application, at scale. By session’s end, you gain an understanding of the benefits of Amazon Lightsail; when to choose Amazon Lightsail or Amazon EC2; best practices for deploying your application onto Lighsail preconfigured blueprints; how to use an OS-only blueprint to deploy virtually any software package; and how to leverage snapshots and load balancers to scale applications. 

In this workshop, you gain hands-on experience in training deep learning neural networks with Amazon SageMaker using GPU-based EC2 P3 instances. Amazon EC2 P3 instances, powered by NVIDIA Tesla V100 GPUs, offer the highest-performing GPU-based instances in the cloud for efficient model training. We discuss and work through building convolution neural networks for solving common computer vision problems.

While stateless services are suitable for many architectures, stateful services are also useful and sometimes overlooked. In this session, we hear from Netflix about the unique challenges of upgrading stateful services in the cloud, architectural advice to make iterating on stateful services easy, and concrete tools and infrastructure you can use on AWS to make upgrading easy.

In this workshop, we step through the process of deploying and hosting machine learning (ML) models with AWS Lambda and get on-demand inferences. Given a demonstrative dataset, we build and train a simple ML classification model with Amazon SageMaker. Then, we host this model in an AWS Lambda function and expose an inference endpoint through Amazon API Gateway. Finally, we build a pipeline for automating model deployment to Lambda leveraging AWS CodeBuild, AWS CodeDeploy, and AWS CodePipeline.

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we dive deep into best practices for Kinesis Data Streams and Kinesis Data Firehose to get the most performance out of your data streaming applications. Comcast uses Amazon Kinesis Data Streams to build a Streaming Data Platform that centralizes data exchanges. It is foundational to the way our data analysts and data scientists derive real-time insights from the data. In the second part of this talk, Comcast zooms into how to properly scale a Kinesis stream. We first list the factors to consider to avoid scaling issues with standard Kinesis stream consumption, and then we see how the new fan-out feature changes these scaling considerations.

In this workshop, you learn how to build compliance into your CI/CD pipeline. Take a list of real-world HIPAA and PCI requirements, and build a continuous deployment pipeline that ensures compliance and best-practices architecture using AWS services such as AWS CodeStar, AWS Config, Amazon Inspector, AWS X-Ray, AWS Lambda, Amazon S3, AWS Glue, and more. You also have the opportunity to write custom AWS Config rules and ensure that your application is proactively compliant in your builds.