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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Observability empowers you to proactively monitor and analyze your generative AI applications, and evaluation helps you collect feedback, refine models, and enhance output quality. Security – The solution uses AWS services and adheres to AWS Cloud Security best practices so your data remains within your AWS account.

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42 Best Customer Feedback Software for 2022

ProProfs Blog

Yes, you can collect their feedback on your brand offerings with simple questions like: Are you happy with our products or services? Various customer feedback tools help you track your customers’ pulse consistently. What Is a Customer Feedback Tool. Read more: 12 Channels to Capture Customer Feedback. Here we go!

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Feedback loop implementation: Create a mechanism to continuously update the verified cache with new, accurate responses. About the Authors Dheer Toprani is a System Development Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

Diverse feedback is also important, so think about implementing human-in-the-loop testing to assess model responses for safety and fairness. Regular evaluations allow you to adjust and steer the AI’s behavior based on feedback and performance metrics. For each model, you can explicitly allow or deny access to actions.

APIs 101
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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning

Extracting valuable insights from customer feedback presents several significant challenges. Scalability becomes an issue as the amount of feedback grows, hindering the ability to respond promptly and address customer concerns. Large language models (LLMs) have transformed the way we engage with and process natural language.

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Alida gains deeper understanding of customer feedback with Amazon Bedrock

AWS Machine Learning

Alida helps the world’s biggest brands create highly engaged research communities to gather feedback that fuels better customer experiences and product innovation. Open-ended survey questions allow responders to provide context and unanticipated feedback. This post is co-written with Sherwin Chu from Alida.

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