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Knowledge Bases for Amazon Bedrock now supports metadata filtering to improve retrieval accuracy

AWS Machine Learning

To refine the search results, you can filter based on document metadata to improve retrieval accuracy, which in turn leads to more relevant FM generations aligned with your interests. You can apply filters to your retrievals, instructing the vector store to pre-filter based on document metadata and then search for relevant documents.

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Product Update: Summarizer and Quick Filters

Thematic

” Our Summarizer feature is now available to all users with access to Analysis and Dashboard, along with a set of Quick filters that make the summaries really specific and actionable. You can use Summarizers for any datapoint, to quickly grasp what matters for any set of filters you select.

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Overcoming deception: Google’s AI expertly filters out fake reviews

BirdEye

Birdeye offers a wide range of resources designed to assist businesses in gathering genuine reviews, managing their digital footprint, and adhering to Google’s dynamic guidelines. The post Overcoming deception: Google’s AI expertly filters out fake reviews appeared first on Birdeye Customer Experience Management.

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Building AI chatbots using Amazon Lex and Amazon Kendra for filtering query results based on user context

AWS Machine Learning

In some scenarios, you need the search results to be filtered based on the context of the user making the search. Using this setup, when you sign in using User 1 in Department A, search results will be filtered documents belonging to Department A and vice versa for Department B users.

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Resource Management: An Inside Look

Andrew Mcfarland

Resource management is essential to any business, and resource managers are key players in optimizing resources used in a successful organization. By understanding resource management, professionals can gain insight into maximizing their resources for maximum efficiency and productivity.

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Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker

AWS Machine Learning

Amazon SageMaker is a fully managed ML platform in the cloud that equips our enterprise customers with tools and resources to establish cost allocation measures and improve visibility into detailed cost and usage by your teams, business units, products, and more. Use the tags to track and filter for cost allocation reporting.

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5 Key Learnings from Customer Experience Experts at The XI Forum Europe 2023

InMoment XI

Concentrate your resources on these areas to create a more significant impact. By filtering out irrelevant or redundant data, you can streamline your efforts and focus on what truly matters. Understand what truly matters to your customers and where their pain points lie.