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5 Top Customer Service Articles for the Week of April 22, 2019

ShepHyken

Each week I read a number of customer service and customer experience articles from various resources. This article is an interesting case study on Quicken Loans, who have found themselves on both lists. Here are my top five picks from last week. Why Anxious Customers Prefer Human Customer Service by Michelle A.

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Customer Service Training: 12 Things to Keep in Mind When Training Newbies

Nicereply

Customer service isn’t just a box to check. But they don’t stop there—they go the extra mile, addressing every concern and even proactively suggesting additional resources and tips to help the customer be more successful. In addition to tools, creating resources for “in the moment” guidance is also helpful.

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Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator

AWS Machine Learning

We finish with a case study highlighting the benefits realize by a large AWS and PwC customer who implemented this solution. SageMaker includes Amazon SageMaker JumpStart , which offers out-of-the-box solution patterns for organizations seeking to accelerate their MLOps journey. The following diagram illustrates the workflow.

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Reduce energy consumption of your machine learning workloads by up to 90% with AWS purpose-built accelerators

AWS Machine Learning

The Carbontracker study estimates that training GPT-3 from scratch may emit up to 85 metric tons of CO2 equivalent, using clusters of specialized hardware accelerators. Therefore, we used common customer-inspired ML use cases for benchmarking and testing. The results are reported in the following sections.

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­­­­How Sleepme uses Amazon SageMaker for automated temperature control to maximize sleep quality in real time

AWS Machine Learning

Using ML to improve sleep in real time Sleepme is a science-driven organization that uses scientific studies, international journals, and cutting-edge research to bring customers the latest in sleep health and wellness. This use case demanded an ML model that served real-time inference.

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Exploring summarization options for Healthcare with Amazon SageMaker

AWS Machine Learning

See the following case study to learn more about a real-world use case. Building custom models can offer greater flexibility and control over the summarization process, but also requires more time and resources compared to approaches that start from pre-trained models.

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Introducing Survey Translations: Create multilingual surveys for global customer and employee feedback

delighted

Use cases by survey type. As your business makes the leap into uncharted territory, leverage any of Delighted’s out-of-the-box surveys in the local market to understand customer and employee sentiment. This resource has some illustrative examples for Chinese and Spanish. Set internal benchmarks per market.

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