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Transform, analyze, and discover insights from unstructured healthcare data using Amazon HealthLake

AWS Machine Learning

Healthcare data is complex and siloed, and exists in various formats. We store the final output in Fast Healthcare Interoperability Resources (FHIR) compatible format in Amazon HealthLake , making it available for downstream analytics. Users can create meaningful analyses and run interactive analytics using Amazon Athena.

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

ML Engineer at Tiger Analytics. The EventBridge model registration event rule invokes a Lambda function that constructs an email with a link to approve or reject the registered model. The Lambda function dynamically constructs an email for an approval of the model with a link to an API Gateway endpoint to another Lambda function.

APIs 100
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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. The underlying principle of these approaches involves the construction of prompts that encapsulate the recommendation task, user profiles, item attributes, and user-item interactions.

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Call Center Quality Management: A Comprehensive Guide to Improving Customer Satisfaction and Agent Performance

NobelBiz

How to Implement Effective Call Center Quality Management Call Center Management Best Practices What is Call Center Quality Management? Some industries, such as healthcare and finance, have strict regulatory requirements that call centers must adhere to. Feedback should be specific, constructive, and actionable.

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Call Center Agent Feedback: Tips & Best Practices for Providing Effective Agent Feedback

Callminer

Tools like interaction analytics can help call center managers identify relevant issues and deliver precise, targeted feedback to agents and have a more direct impact on metrics like call handling time. Check out some more information about agent performance from our vault here: Expert Tips & Best Practices for Effective Agent Feedback.

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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. He works with government, non-profit, and education customers on big data and analytical projects, helping them build solutions using AWS.

Scripts 96
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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.