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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

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. To evaluate the performance of the models, we used multiple techniques.

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Get insights on your user’s search behavior from Amazon Kendra using an ML-powered serverless stack

AWS Machine Learning

For example, you may want to group similar queries such as “What is Amazon Kendra” and “What is the purpose of Amazon Kendra” together so that you can effectively analyze the metrics and gain a deeper understanding of the data. The Lambda functions upload the search metrics to an Amazon Simple Storage Service (Amazon S3) bucket.

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Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS

AWS Machine Learning

ResourceId=resource_id, # Endpoint name ScalableDimension="sagemaker:variant:DesiredInstanceCount", # SageMaker supports only Instance Count PolicyType="TargetTrackingScaling", # 'StepScaling'|'TargetTrackingScaling' TargetTrackingScalingPolicyConfiguration={ "TargetValue": 5.0, # The target value for the metric.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

In this innovation talk, hear how the largest industries, from healthcare and financial services to automotive and media and entertainment, are using generative AI to drive outcomes for their customers. See demos on how to build analytics dashboards and integrations between LLMs and Amazon QuickSight to visualize your key metrics.