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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. Bill Dettering is the CEO and Founder of Zingtree , a SaaS solution for building interactive decision trees and agent scripts for contact centers (and many other industries). Interactive agent scripts from Zingtree solve this problem. Bill Dettering.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

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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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The Case For the Anti-Script: A Multifactor Analysis of Script Adherence

Balto

“The anti-script doesn’t mean that you should wing it on every call… what anti-script means is, think about a physical paper script and an agent who is reading it off word for word… you’re taking the most powerful part of the human out of the human.” Share on Twitter. Share on Facebook.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

The following code illustrates this policy, but don’t add it to the shared services account yet: #Data Science account's policy to access Shared Services' S3 bucket { 'Version': '2012-10-17', 'Statement': [{ 'Sid': 'AddPerm', 'Effect': 'Allow', 'Principal': { 'AWS': 'arn:aws:iam:: :root' }, "Action": [. 's3:PutObject',

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Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension

AWS Machine Learning

Migrating from interactive development on notebooks to batch jobs required you to copy code snippets from the notebook into a script, package the script with all its dependencies into a container, and schedule the container to run. In the following section, we show an example of using initialization scripts to install packages.

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AWS Transcribe With Nexmo Voice Using PHP

Nexmo

In this tutorial, we’ll use a Nexmo Voice number to create a callback script that interacts with a caller to prompt for a voice message. Though the built-in web server should not be used in a production environment, it is fine for sample scripts like this. Make sure to replace {bucket_name} with the actual bucket name.

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