Remove 2012 Remove Accountability Remove Personalization
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On Being an Accountable Customer Service Leader

Customer Service Life

Properly authenticating the account. Leaving complete account notes for the next person who interacts with the customer. This exercise reminded me of the time when we started this blog back in 2012. Starting a blog about customer service became instant accountability for me. Quality as accountability.

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Recommend top trending items to your users using the new Amazon Personalize recipe

AWS Machine Learning

Amazon Personalize is excited to announce the new Trending-Now recipe to help you recommend items gaining popularity at the fastest pace among your users. Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users.

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Cautionary tale—BNPL disrupts with faster, smarter, more personalized payment solutions

Maru Group

The global market continues to put extreme pressure on financial institutions to develop faster, smarter, more personalized payment solutions. As early as 2012, fintechs provided a viable alternative to traditional financial institutions for purchases ranging from home appliances to travel packages. Ready to jumpstart your CX program?

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Create a data labeling project with Amazon SageMaker Ground Truth Plus

AWS Machine Learning

Overview of solution First, you fill out a consultation form on the Get Started with Amazon SageMaker Ground Truth page or, if you already have an AWS account, you submit a request project form on the SageMaker Ground Truth Plus console. Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account.

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Detect and protect sensitive data with Amazon Lex and Amazon CloudWatch Logs

AWS Machine Learning

In today’s digital landscape, the protection of personally identifiable information (PII) is not just a regulatory requirement, but a cornerstone of consumer trust and business integrity. These policies allow to audit and mask sensitive data that appears in log events ingested by the log groups in your account.

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Use Amazon SageMaker Studio with a custom file system in Amazon EFS

AWS Machine Learning

This scenario offers the following benefits: Individual data storage and analysis – Users can store their personal datasets, models, and other files in their private spaces, allowing them to work on their own projects independently. Refer to Creating a trail for your AWS account for additional information.

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Deploy a Microsoft Teams gateway for Amazon Q, your business expert

AWS Machine Learning

In this post, we walk you through the process to deploy Amazon Q business expert in your AWS account and add it to Microsoft Teams. In the following sections, we show how to deploy the project to your own AWS account and Teams account, and start experimenting! Everything you need is provided as open source in our GitHub repo.