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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

However, as a new product in a new space for Amazon, Amp needed more relevant data to inform their decision-making process. Part 1 shows how data was collected and processed using the data and analytics platform, and Part 2 shows how the data was used to create show recommendations using Amazon SageMaker , a fully managed ML service.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

This data is information rich but can be vastly heterogenous. Proper handling of specialized terminology and concepts in different formats is essential to detect insights and ensure analytical integrity. We are excited about the future ahead, and your feedback will play a vital role in guiding the progress of this product.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

The applications also extend into retail, where they can enhance customer experiences through dynamic chatbots and AI assistants, and into digital marketing, where they can organize customer feedback and recommend products based on descriptions and purchase behaviors.

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How to Be 2 Steps Ahead in Anticipating Your Customer Needs

Kayako

In-flight entertainment has been in-demand since commercial flight became routine; Virgin just figured out a way to up the ante by including individual TVs in all headrests. Predictive analytics looks at the actions both you and your past customers have taken at different stages of the customer journey. Consideration.

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50+ Customer Engagement Statistics for 2020

ProProfs Blog

In the category of online entertainment, Netflix was the leading brand with a ranking of 89 percent in terms of how the brand met consumer expectations versus the consumer-generated, category-specific ideal. This stat describes that if you’ve really worked on active engagement with your customers, they will leave awesome feedback for you.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

If you further automatically fine-tune a model based on user feedback (or other end-user-controllable information), you must consider if a malicious threat actor could change the model arbitrarily based on manipulating their responses and achieve training data poisoning. Ram Vittal is a Principal ML Solutions Architect at AWS.