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How to Write an After-Call Survey Script

Fonolo

The value is in the timing—customers will give the most accurate accounts of their service experiences shortly after they’ve happened. Let’s say your customers are patients at a healthcare facility. You might have a carefully crafted questionnaire or script for your after-call survey. Sample After-Call Survey Script.

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Exploring summarization options for Healthcare with Amazon SageMaker

AWS Machine Learning

In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. In a healthcare setting, this would mean giving the model some data including phrases and terminology pertaining specifically to patient care.

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

AWS Machine Learning

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

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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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Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

AWS Machine Learning

In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. In the following sections, we go through the steps to prepare your training data, create a training script, and run a SageMaker training job.

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Everything You Need to Know About Auto Attendant

Hodusoft

In this blog post, we will discuss everything about auto attendants starting from what they are, how they work, the pros and cons of using auto attendants, how to set up an auto attendant, and how to include scripts. Past and Present: A Brief History of Auto Attendant How Does an Auto Attendant Work?