Remove Construction Remove Feedback Remove Metrics
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Uncovering the True Enemy of Great Customer Service with Brian Hamilton

ShepHyken

How can companies get better customer feedback? How can businesses move beyond customer satisfaction metrics? Your employees are just as valuable sources of feedback as your customers. While customer surveys and digital feedback are helpful, the most valuable insights often come from direct observation and conversations.

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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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35 Indicators that a Company Isn’t Customer-Centric

CX Accelerator

11) have zero channels for customer feedback. Tanuj Diwan , advising that we listen to all customers with an open mind says: 17) get offended by customer complaints, feedback, and do nothing about them. 11) have zero channels for customer feedback. 32) can’t take complaints constructively. 5) put profits before purpose.

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The Amazon D&C team implemented the solution in a pilot for Amazon engineers and collected user feedback. During the pilot, users provided 118 feedback responses.

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A generative AI-powered solution on Amazon SageMaker to help Amazon EU Design and Construction

AWS Machine Learning

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon Warehouses across Europe and the MENA region. Fine-tuned LLM – We constructed the training dataset from the documents and contents and conducted fine-tuning on the foundation model.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Thanks to this construct, you can evaluate any LLM by configuring the model runner according to your model. Evaluation algorithm Computes evaluation metrics to model outputs.

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I am so Frustrated! Customers’ Comments Don’t Reflect the Score They Give Me. Why?

Beyond Philosophy

While NPS is beneficial in many ways, people focus too hard on the metric and lose sight of the big picture. Customer behavior is complicated, particularly regarding providing positive feedback. Not exactly what you would expect from a construction market customer, but there it was. .