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How to Make or Break Your Customer Experience

Beyond Philosophy

Let’s say your IT system requires getting your email address for every customer to access the details of the account. When you are frustrated, stressed, and upset, how do you feel about entering your account number followed by the pound sign? In many cases, they will also use a Call Center script. Let me give you an example.

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Conversational Customer Service Scripts from Dunder Mifflin (+ Examples)

SharpenCX

It was the small business feel that kept accounts with Dunder Mifflin. Stiff scripts and robotic conversations don’t give your customers the warm fuzzies. Research shows making agents adhere to rigid customer service scripts is a leading source of customer frustration. . The script to kick off any interaction.

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Build an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation

AWS Machine Learning

Prerequisites To implement the solution provided in this post, you should have the following: An active AWS account and familiarity with FMs, Amazon Bedrock, and OpenSearch Serverless. While running deploy.sh, if you provide a bucket name as an argument to the script, it will create a deployment bucket with the specified name.

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Introducing Amazon SageMaker HyperPod to train foundation models at scale

AWS Machine Learning

Solution overview To deploy your SageMaker HyperPod, you first prepare your environment by configuring your Amazon Virtual Private Cloud (Amazon VPC) network and security groups, deploying supporting services such as FSx for Lustre in your VPC, and publishing your Slurm lifecycle scripts to an S3 bucket. Choose Create role. Choose Save.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

By performing operations (applications, infrastructure) as code, you can provide consistent and reliable deployments in multiple AWS accounts and AWS Regions, and maintain versioned and auditable infrastructure configurations. It calls the CreateDataSource and DeleteDataSource APIs. Nitin Eusebius is a Sr.

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Host the Whisper Model on Amazon SageMaker: exploring inference options

AWS Machine Learning

Next, we create custom inference scripts. Within these scripts, we define how the model should be loaded and specify the inference process. With the model artifacts, custom inference scripts and selected DLCs, we’ll create Amazon SageMaker models for PyTorch and Hugging Face respectively. In the custom inference.py

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