Remove Auto-complete Remove Automation Remove Metadata
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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

Metadata 123
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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

Localization relies on both automation and humans-in-the-loop in a process called Machine Translation Post Editing (MTPE). When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata. One of LLMs most fascinating strengths is their inherent ability to understand context.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle. This includes file type verification, size validation, and metadata extraction before routing to Amazon Textract. Follow Octus on LinkedIn and X.

DevOps 88
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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

The automation provided by Rad AI Impressions not only reduces burnout, but also safeguards against errors arising from manual repetition. For years, Rad AI has been a reliable partner to radiology practices and health systems, consistently delivering high availability and generating complete results seamlessly in 0.5–3

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Scale AI training and inference for drug discovery through Amazon EKS and Karpenter

AWS Machine Learning Blog

The platform both enables our AI—by supplying data to refine our models—and is enabled by it, capitalizing on opportunities for automated decision-making and data processing. We use Amazon EKS and were looking for the best solution to auto scale our worker nodes. This enables all steps to be completed from a web browser.

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning Blog

With a decade of enterprise AI experience, Veritone supports the public sector, working with US federal government agencies, state and local government, law enforcement agencies, and legal organizations to automate and simplify evidence management, redaction, person-of-interest tracking, and eDiscovery.

Metadata 132
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. It can take up to 20 minutes for the setup to complete.

ML 98