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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. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units.

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Establishing an AI/ML center of excellence

AWS Machine Learning

By taking a proactive approach , the CoE provides ethical compliance but also builds trust, enhances accountability, and mitigates potential risks such as veracity, toxicity, data misuse, and intellectual property concerns. Platform – A central platform such as Amazon SageMaker for creation, training, and deployment.

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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.

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Never miss an issue with Spearline alerts

Spearline

Bud Lee Director, Software Quality Engineering at 8X8 What are Spearline alerts? Using a custom value, the Spearline country benchmark, or the previous time period average as the threshold. Intrado works with some of the world’s largest organizations, from banks to healthcare providers, retailers to logistics companies.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning

To deliver this, we use Step Functions to create a state machine that trains the models with the latest data, checks their performance on a benchmark set, and redeploys the models if they have improved. The Petfinder team at Purina wants an automated solution that they can deploy with minimal maintenance.

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Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

Based on 10 years of historical data, hundreds of thousands of face-offs were used to engineer over 70 features fed into the model to provide real-time probabilities. By continuously listening to NHL’s expertise and testing hypotheses, AWS’s scientists engineered over 100 features that correlate to the face-off event.

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Evaluation of generative AI techniques for clinical report summarization

AWS Machine Learning

In this part of the blog series, we review techniques of prompt engineering and Retrieval Augmented Generation (RAG) that can be employed to accomplish the task of clinical report summarization by using Amazon Bedrock. When summarizing healthcare texts, pre-trained LLMs do not always achieve optimal performance.