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amazon-redshift-tiered-storage icon amazon-redshift-tiered-storage

Amazon Redshift offers a common query interface against data stored in fast, local storage as well as data from high-capacity, inexpensive storage (S3). This workshop will cover the basics of this tiered storage model and outline the design patterns you can leverage to get the most from large volumes of data. You will build out your own Redshift cluster with multiple data sets to illustrate the trade-offs between the storage systems. By the time you leave, you’ll know how to distribute your data and design your DDL to deliver the best data warehouse for your business.

amazon-rekognition-custom-labels-a2i-automated-continuous-model-improvement icon amazon-rekognition-custom-labels-a2i-automated-continuous-model-improvement

With Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.

amazon-rekognition-video-analyzer icon amazon-rekognition-video-analyzer

A working prototype for capturing frames off of a live MJPEG video stream, identifying objects in near real-time using deep learning, and triggering actions based on an objects watch list.

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