Remove Construction Remove Metrics Remove Scripts
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Why Do Agents Go Off Script? Mistakes vs Improvisation

Balto

When agents intentionally go off script, it’s because they are improvising to get a better call outcome and should be encouraged. In 2022, we published our findings on why agents intentionally go off their scripts. Why Agents Go Off Script. Figure 3: Why do agents go off script? Key Takeaways.

Scripts 52
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The Customer Success Maturity Model Part 2: “Operationalize” Capabilities (Constructing Your CS System)

Education Services Group

Constructing and evolving these processes is the second category of capabilities on the ESG Customer Success Maturity Model. Metrics that track your customers’ experience are crucial to the stability and longevity of your CS organization. Let’s break that down a bit. CX (NPS, CSAT, etc.).

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The Key Role of Call Center Dynamic Agent Scripting in Customer Experience

NobelBiz

Scripts are an essential component of every contact center. The correct amount of data and accurate information delivery can yield impressive scripting capabilities. To provide a better customer experience (CX), dynamic agent scripting is required. Table of Contents show What is call center Dynamic Agent Scripting?

Scripts 52
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Create a document lake using large-scale text extraction from documents with Amazon Textract

AWS Machine Learning

The first allows you to run a Python script from any server or instance including a Jupyter notebook; this is the quickest way to get started. The second approach is a turnkey deployment of various infrastructure components using AWS Cloud Development Kit (AWS CDK) constructs. We have packaged this solution in a.ipynb script and.py

Scripts 107
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

The following diagram depicts an architecture for centralizing model governance using AWS RAM for sharing models using a SageMaker Model Group , a core construct within SageMaker Model Registry where you register your model version. The ML admin sets up this table with the necessary attributes based on their central governance requirements.

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Build an air quality anomaly detector using Amazon Lookout for Metrics

AWS Machine Learning

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

Metrics 81
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Node problem detection and recovery for AWS Neuron nodes within Amazon EKS clusters

AWS Machine Learning

The node recovery agent is a separate component that periodically checks the Prometheus metrics exposed by the node problem detector. Additionally, the node recovery agent will publish Amazon CloudWatch metrics for users to monitor and alert on these events. You can see the CloudWatch NeuronHasError_DMA_ERROR metric has the value 1.