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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning

Although we use a specific algorithm to train the model in our example, you can use any algorithm that you find appropriate for your use case. The following screenshot shows the example user profile for this post. Enter the following script in the editor, providing the ARN for the secret you created earlier: #!/bin/bash

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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

Specifically, we use the example of architecture diagrams for complex images due to their incorporation of numerous different visual icons and text. For example, if a user tried to search for a specific type of blue bottle, results of many different types of blue bottles will be displayed.

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Source: Human Resource Management; Issue: 51(4); 2012; Pages 535-548. Example: Campaign A has a high call volume but campaign B has less calls and the agents that are assigned campaign B are not busy. Interactive agent scripts from Zingtree solve this problem. Bill Dettering. This is even more critical for BPOs. Jeff Greenfield.

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Use a custom image to bring your own development environment to RStudio on Amazon SageMaker

AWS Machine Learning

You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. You can use the sample script create-and-update-image.sh. Choose Delete.

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Is the Contact Center a Good Career? Tenured Agents Say Yes

Balto

When I worked in service roles, I had a script, and I knew what I had to do to have a successful social interaction with a customer. This helped me build confidence through a body of evidence — you use your script correctly as a waitress and you get a dopamine hit in the form of a tip. 2012, December 20). 2022, June 23).

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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning

SageMaker runs the legacy script inside a processing container. SageMaker takes your script, copies your data from Amazon Simple Storage Service (Amazon S3), and then pulls a processing container. The SageMaker Processing job sets up your processing image using a Docker container entrypoint script.

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Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support

AWS Machine Learning

You can run the script by choosing Run in Code Editor or using the CLI in a JupyterLab terminal. For example, DLC-based framework images are Ubuntu based, in which the following instructions would work. LCCs are scripts that SageMaker runs during events like space creation.

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