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Exploring summarization options for Healthcare with Amazon SageMaker

AWS Machine Learning

In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. In a healthcare setting, this would mean giving the model some data including phrases and terminology pertaining specifically to patient care.

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What is Call Center Compliance?

NobelBiz

call centers that handle patient information on behalf of healthcare providers and organizations are obligated to comply with the Health Insurance Portability and Accountability Act (HIPAA) that sets standards for the security and privacy of sensitive patient health information. HIPAA Regulations: In the U.S.,

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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning

Customers are now pre-training and fine-tuning LLMs ranging from 1 billion to over 175 billion parameters to optimize model performance for applications across industries, from healthcare to finance and marketing. For more information on how to enable SMP with your existing PyTorch FSDP training scripts, refer to Get started with SMP.

Scripts 97
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs 111
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Explain medical decisions in clinical settings using Amazon SageMaker Clarify

AWS Machine Learning

The intent is to offer an accelerated path to adoption of predictive techniques within CDSSs for many healthcare organizations. Technical background A large asset for any acute healthcare organization is its clinical notes. You also use a custom inference script to do the predictions within the container.

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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

In order to run inference through SageMaker API, make sure to pass the Predictor class. pre_trained_model = Model( image_uri=deploy_image_uri, model_data=pre_trained_model_uri, role=aws_role, predictor_cls=Predictor, name=pre_trained_name, env=large_model_env, ) # Deploy the pre-trained model.

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Configure and use defaults for Amazon SageMaker resources with the SageMaker Python SDK

AWS Machine Learning

Enterprise customers in tightly controlled industries such as healthcare and finance set up security guardrails to ensure their data is encrypted and traffic doesn’t traverse the internet. Additionally, each API call can have its own configurations. Then it copies the file into the default location for Studio notebooks.

APIs 78