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  • Data and model quality monitoring with Amazon SageMaker Model . . .
    Amazon SageMaker Model Monitor monitors the quality of Amazon SageMaker AI machine learning models in production With Model Monitor, you can set up: Continuous monitoring with a real-time endpoint Continuous monitoring with a batch transform job that runs regularly On-schedule monitoring for asynchronous batch transform jobs
  • Monitoring in-production ML models at large scale using . . .
    After you deploy your model, you can use Amazon SageMaker Model Monitor to continuously monitor the quality of your ML model in real time You can also configure alerts to notify and trigger actions if any drift in model performance is observed
  • Model Monitoring with Amazon SageMaker - Medium
    To capture and store inference input and output, enable Model Monitor’s data capture tool on a Amazon SageMaker Endpoint Model Monitor analyzes the data collected at a regular frequency
  • GitHub - aws-samples amazon-sagemaker-model-monitor-using . . .
    In the repository we present a comprehensive example of how you can leverage the Amazon SageMaker Model Monitor capabilities when you are developing and deploying your models using your own environment (cloud or on-prem) and are looking to SageMaker specifically to help you with monitoring your models
  • AWS SageMaker Model Monitoring Guide - bdrsuite. com
    Model monitoring plays a crucial role in the lifecycle of machine learning (ML) models It involves tracking and assessing the performance of deployed models to ensure they continue to meet the desired expectations
  • SageMaker Model Monitor - Amazon SageMaker AI
    Model Monitor helps you maintain model quality by detecting violations of user-defined thresholds for data quality, model quality, bias drift and feature attribution drift In addition, you can configure alerts so you can troubleshoot violations as they arise and promptly initiate retraining
  • Amazon SageMaker model monitor: A system for real-time . . .
    Our system automatically detects data, concept, bias, and feature attribution drift in models in real-time and provides alerts so that model owners can take corrective actions and thereby maintain high quality models





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