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Foundational vision models and visual prompt engineering for autonomous driving applications

AWS Machine Learning

For example, we can use an API chain to call an API and invoke an LLM to answer the question based on the API response. The bounding box area thresholds are defined by the Common Objects in Context (COCO) evaluation metrics [Lin et al., It helps chain the data sources and an LLM to produce the output.

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Machine Learning with MATLAB and Amazon SageMaker

AWS Machine Learning

It’s heavily used in many industries such as automotive, aerospace, communication, and manufacturing. After checking the accuracy metrics for the locally-trained model, we can move the training into Amazon SageMaker. In recent years, MathWorks has brought many product offerings into the cloud, especially on Amazon Web Services (AWS).

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Build a solution for a computer vision skin lesion classifier using Amazon SageMaker Pipelines

AWS Machine Learning

AWS offers a pre-trained and fully managed AWS AI service called Amazon Rekognition that can be integrated into computer vision applications using API calls and require no ML experience. You just have to provide an image to the Amazon Rekognition API and it can identify the required objects according to pre-defined labels.

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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.

APIs 81
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Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

AWS Machine Learning

Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. The neural forecasters can be bundled as a single ensemble model, or incorporated individually into Bosch’s model universe, and accessed easily via REST API endpoints.

APIs 81