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How to Transform the Customer Experience

ShepHyken

In Matt’s words, “The world is now on-demand and highly personalized. Personalization is a hot topic, and “on-demand” is about giving the customer what they want when they want it. It is a prime example of being easy and frictionless. ® is another great example. someone—actually, most people—will say, “Amazon.”

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

The personalization of LLM applications can be achieved by incorporating up-to-date user information, which typically involves integrating several components. In this post, we elucidate the simple yet powerful idea of combining user profiles and item attributes to generate personalized content recommendations using LLMs.

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Guest Post: 4 Ways to Transform Customer Experience While Growing Rapidly

ShepHyken

In April 2021, I had the honor of becoming the chief executive officer of Xpress Global Systems (XGS), a trucking company that specializes in transporting flooring products across all 50 of the United States. This is often a tough act to balance: rapidly scaling up business while still providing personalized customer service.

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Databricks DBRX is now available in Amazon SageMaker JumpStart

AWS Machine Learning

When you select the option to use the SDK, you will see example code that you can use in the notebook editor of your choice in SageMaker Studio. Also make sure you have the account-level service limit for using ml.p4d.24xlarge In this section, we provide some example prompts and sample output. 24xlarge or ml.pde.24xlarge

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Align feedback insights with your company goals with category grouping

Hello Customer

Our AI already takes your industry and touchpoint into account, still, it’s possible that you’re looking for a more personalized analysis. For example: what if the feedback categories don’t fully match with your internal KPIs ? Simply add or remove categories from your personalized groups in a few clicks.

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Large language model inference over confidential data using AWS Nitro Enclaves

AWS Machine Learning

This post discusses how Nitro Enclaves can help protect LLM model deployments, specifically those that use personally identifiable information (PII) or protected health information (PHI). In our example use case, an LLM service is designed to answer employee healthcare benefit questions or provide a personal retirement plan.

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Develop generative AI applications to improve teaching and learning experiences

AWS Machine Learning

Generative AI and natural language programming (NLP) models have great potential to enhance teaching and learning by generating personalized learning content and providing engaging learning experiences for students. For our example, a teacher inputs the Kids and Bicycle Safety guidelines from the United States Department of Transportation.

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