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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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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. You can also refer to our example notebooks to get started. Training performant models at this scale can be a challenge.

Scripts 99
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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.

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning

Dataset collection We followed the methodology outlined in the PMC-Llama paper [6] to assemble our dataset, which includes PubMed papers sourced from the Semantic Scholar API and various medical texts cited within the paper, culminating in a comprehensive collection of 88 billion tokens. Create and launch ParallelCluster in the VPC.

APIs 102
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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

For example, we could use a specialized language model pre-trained on clinical reports from scratch. The following figure is an example of a radiology report. To fine-tune this model through SageMaker Jumpstart, labeled examples must be provided in the form of {prompt, completion} pairs.

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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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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. Provide a name for the stack (for example, networking-stack ), and complete the remaining steps to create the stack.

APIs 79