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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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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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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 83
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GenASL: Generative AI-powered American Sign Language avatars

AWS Machine Learning

The GenASL web app invokes the backend services by sending the S3 object key in the payload to an API hosted on Amazon API Gateway. API Gateway instantiates an AWS Step Functions The state machine orchestrates the AI/ML services Amazon Transcribe and Amazon Bedrock and the NoSQL data store Amazon DynamoDB using AWS Lambda functions.

APIs 123
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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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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. The approver approves the model by following the link in the email to an API Gateway endpoint. API Gateway invokes a Lambda function to initiate model updates.

APIs 125