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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 EventBridge model registration event rule invokes a Lambda function that constructs an email with a link to approve or reject the registered model.

APIs 100
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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

For a quantitative analysis of the generated impression, we use ROUGE (Recall-Oriented Understudy for Gisting Evaluation), the most commonly used metric for evaluating summarization. This metric compares an automatically produced summary against a reference or a set of references (human-produced) summary or translation.

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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Healthcare and life sciences. Fraud detection.

Scripts 71
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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

In this scenario, the generative AI application, designed by the consumer, must interact with the fine-tuner backend via APIs to deliver this functionality to the end-users. If an organization has no AI/ML experts in their team, then an API service might be better suited for them. 15K available FM reference Step 1.

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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

We also share the key technical challenges that were solved during construction of the Face-off Probability model. The decision tree provided the cut-offs for each metric, which we included as rules-based logic in the streaming application. At the end, we found that the LightGBM model worked best with well-calibrated accuracy metrics.

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Create a document lake using large-scale text extraction from documents with Amazon Textract

AWS Machine Learning

AWS customers in healthcare, financial services, the public sector, and other industries store billions of documents as images or PDFs in Amazon Simple Storage Service (Amazon S3). The second approach is a turnkey deployment of various infrastructure components using AWS Cloud Development Kit (AWS CDK) constructs.

Scripts 90
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Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

AWS Machine Learning

Another example might be a healthcare provider who uses PLM inference endpoints for clinical document classification, named entity recognition from medical reports, medical chatbots, and patient risk stratification. The performance of the architecture is typically measured using metrics such as validation loss.

Metrics 98