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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Set up Amazon SageMaker Studio with Jupyter Lab 3 using the AWS CDK

AWS Machine Learning

AWS CDK constructs are the building blocks of AWS CDK applications, representing the blueprint to define cloud architectures. You have permissions to create and deploy AWS CDK and AWS CloudFormation resources as defined in the scripts outlined in the post. AWS CDK scripts. Studio construct file. The AWS CDK is installed.

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5 Do’s and Don’ts for Call Center Appointment Setting

Quality Contact Solutions

Yes, it would help if you came into a call as prepared as possible, but remember that the other person on the line doesn’t know your script. Make sure to include answers to these questions in your conversations: Who am I? Furthermore, call scripts or guidelines address questions as accurately and quickly as possible.

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Get smarter search results with the Amazon Kendra Intelligent Ranking and OpenSearch plugin

AWS Machine Learning

For this tutorial, you’ll need a bash terminal on Linux , Mac , or Windows Subsystem for Linux , and an AWS account. Create and start OpenSearch using the Quickstart script. script: wget [link] chmod +x search_processing_kendra_quickstart.sh. The script below is used to create an index and load sample documents.

Scripts 79
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Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

AWS Machine Learning

Solution overview A typical training job for deep learning in SageMaker consists of two main steps: preparing a training script and configuring a SageMaker training job launcher. Prerequisites To start using SageMaker with TensorBoard, you need to set up a SageMaker domain with an Amazon VPC under an AWS account. x_test / 255.0

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Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

AWS Machine Learning

For example, in some e-commerce platforms, account registration is wide open. Fraudsters can behave maliciously just once with an account and never use the same account again. Additionally, it’s challenging to construct a streaming data pipeline that can feed incoming events to a GNN real-time serving API.

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Configure an AWS DeepRacer environment for training and log analysis using the AWS CDK

AWS Machine Learning

In this post, we enable the provisioning of different components required for performing log analysis using Amazon SageMaker on AWS DeepRacer via AWS CDK constructs. An administrator can run the AWS CDK script provided in the GitHub repo via the AWS Management Console or in the terminal after loading the code in their environment.

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