ANT322-R – [REPEAT] High Performance Data Streaming with Amazon Kinesis: Best Practices
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. Our customer NICE inContact joins us to discuss how they utilize Amazon Kinesis Data Streams to make real-time decisions on customer contact routing and agent assignments for its Call Center as a Service (CCaaS) Platform. NICE inContact walks through their architecture and requirements for low-latency, accurate processing to be as responsive as possible to changes.
IOT314-R – [REPEAT] IoT Analytics Workshop
In this workshop, you learn about the different components of AWS IoT Analytics. You have the opportunity to configure AWS IoT Analytics to ingest data from AWS IoT Core, enrich the data using AWS Lambda, visualize the data using Amazon QuickSight, and perform machine learning using Jupyter Notebooks. Join us, and build a solution that helps you perform analytics on appliance energy usage in a smart building and forecast energy utilization to optimize consumption.
DVC304 – Red Team vs. Blue Team on AWS
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.
This session is part of re:Invent Developer Community Day, a series led by AWS enthusiasts who share first-hand, technical insights on trending topics.
IOT322-R – [REPEAT] Machine Learning Inference at the Edge
Training ML models requires massive computing resources, so it is a natural fit for the cloud. But, inference typically takes a lot less computing power and is often done in real time when new data is available. So, getting inference results with very low latency is important to making sure your IoT applications can respond quickly to local events. AWS Greengrass ML Inference gives you the best of both worlds. You use ML models that are built and trained in the cloud and you deploy and run ML inference locally on connected devices. For example, you can build a predictive model in Amazon SageMaker for scene detection analysis and then run it locally on an AWS Greengrass enabled security camera device where there is no cloud connectivity to predict and send an alert when an incoming visitor is detected. We show you some examples of image recognition models running on edge devices.
CMP368-R – [REPEAT] Scalable Multi-Node Deep Learning Training in the Cloud
Developing and optimizing machine learning (ML) models is an iterative process. It involves frequently training and retraining models with new data and optimizing model and training parameters to increase prediction accuracy. At the same time, to drive higher prediction accuracy, models are getting larger and more complex, thus increasing the demand for compute resources. In this chalk talk, AWS and Fast.ai will share best practices on how to optimize AWS infrastructure to minimize deep learning training times by using distributed/multi-node training.
CON308-R – [REPEAT] Building Microservices with Containers
Microservices are minimal function services that are deployed separately, but can interact together to function as a broader application. Microservices can be built, changed, and deployed quickly with a relatively small impact, empowering developers to speed up the rate of innovation. In this session, we show how containers help enable microservices-based application architectures, discuss best practices for building new microservices, and cover the AWS services that allow you to build performant microservices applications.
GENM201 – Monday Night Live
Want to enjoy live entertainment while learning about Amazon’s infrastructure updates? Be sure to attend Monday Night Live with Peter DeSantis, Vice President, AWS Global Infrastructure and Customer Support, on Nov. 26, at 7:30 PM at The Venetian, Level 2, Hall A.