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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning

In some use cases, particularly those involving complex user queries or a large number of metadata attributes, manually constructing metadata filters can become challenging and potentially error-prone. The extracted metadata is used to construct an appropriate metadata filter. model in Amazon Bedrock.

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How Valor Intelligent Processing Uses Speech Analytics to Improve CFPB Compliance and Agent Performance

Provana

For the last few years, collection agencies have been using call center speech analytics to help reduce delinquencies, mitigate losses, and maximize their accounts receivable recovery. Having said that, only malleable speech analytics solutions that quickly evolve as per customer preferences lead to better collection yield.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Mastering customer health for complex enterprise relationships

Totango

For enterprises, a well-constructed customer health score isnt just a nice-to-have; its a strategic asset that empowers teams to manage complexity, sustain customer satisfaction, and scale their customer success efforts. The enterprise solution Large customer accounts often have layered needs.

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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

To efficiently use the models context window, we construct a tool selector that retrieves only the relevant tools based on the information in the agent state. With more than 20 years of experience in data analytics and enterprise applications, he has driven technological innovation across both the public and private sectors.

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Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning

At Deutsche Bahn, a dedicated AI platform team manages and operates the SageMaker Studio platform, and multiple data analytics teams within the organization use the platform to develop, train, and run various analytics and ML activities. For high availability, multiple identical private isolated subnets are provisioned.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

In the following sections, we provide a detailed explanation on how to construct your first prompt, and then gradually improve it to consistently achieve over 90% accuracy. Later, if they saw the employee making mistakes, they might try to simplify the problem and provide constructive feedback by giving examples of what not to do, and why.