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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. Regulations in the healthcare industry call for especially rigorous data governance.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents

AWS Machine Learning

Remediating hallucinations is crucial for production applications that use LLMs, particularly in domains where incorrect information can have serious consequences, such as healthcare, finance, or legal applications. After an LLM generates a response, these workflows perform a check to see if hallucinations occurred.

APIs 79
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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. With this launch, customers can now seamlessly share and access ML models registered in SageMaker Model Registry between different AWS accounts.

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Evaluation of generative AI techniques for clinical report summarization

AWS Machine Learning

This is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API. When summarizing healthcare texts, pre-trained LLMs do not always achieve optimal performance.

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John Snow Labs Medical LLMs are now available in Amazon SageMaker JumpStart

AWS Machine Learning

This summarization capability not only boosts efficiency but also makes sure that no critical details are overlooked, thereby supporting optimal patient care and enhancing healthcare outcomes. John Snow Labs is the developer behind Spark NLP, Healthcare NLP, and Medical LLMs. To learn more, refer to the API documentation.

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Fine-tune large language models with Amazon SageMaker Autopilot

AWS Machine Learning

This fine-tuning approach can be extended to other tasks, such as summarization or text generation, in domains like healthcare, education, or financial services. The CreateAutoMLJobV2 API offers a low-level interface that allows for more control and customization.

Metrics 105