Remove web en privacy-policy
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Redact sensitive data from streaming data in near-real time using Amazon Comprehend and Amazon Kinesis Data Firehose

AWS Machine Learning

Due to the breadth and depth of data being ingested from multiple sources, businesses look for solutions to protect their customers’ privacy and keep sensitive data from being accessed from end systems. Redacting PII entities helps you protect your customer’s privacy and comply with local laws and regulations. Choose Attach policies.

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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

response = comprehend.detect_entities( Text=text, LanguageCode='en') #print enitities from the response JSON for entity in response['Entities']: print(f'{entity["Type"]} : {entity["Text"]}'). Insurance claims packages require a lot of privacy compliance and regulations because they contain both PII and PHI data. client('comprehend').

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Retain original PDF formatting to view translated documents with Amazon Textract, Amazon Translate, and PDFBox

AWS Machine Learning

To learn more about IAM permissions see Policies and permissions in IAM as well as How Amazon Textract works with IAM and How Amazon Translate works with IAM. About the Authors Anubha Singhal is a Senior Cloud Architect at Amazon Web Services in the AWS Professional Services organization.

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Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.