newsAWS Machine LearningTrust 88 · LabPublished 1mo agoLive · 1mo ago
Monitoring discriminative ML models using Amazon SageMaker AI with MLflow
Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use open source Evidently together with Amazon SageMaker AI to generate monitoring reports, organize and compare the results in MLflow, scale through pipelines, and trigger drift notifications.
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- PossiblePossibly related (embedding) · 68%mlflow/mlflow →
- PossiblePossibly related (embedding) · 67%aws/sagemaker-python-sdk →
- PossiblePossibly related (embedding) · 62%aws/amazon-sagemaker-examples →
- PossiblePossibly related (embedding) · 58%SwanHubX/SwanLab →
- PossiblePossibly related (embedding) · 58%SuperCowPowers/workbench →
- PossiblePossibly related (embedding) · 49%aws/sagemaker-example-notebooks-testing →
- PossiblePossibly related (embedding) · 54%upgini/upgini →
- PossiblePossibly related (embedding) · 51%hnminh1005/ds-component-pipeline →
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repoaws/sagemaker-example-notebooks-testingrepoupgini/upginirepohnminh1005/ds-component-pipelinerepoORNL/flowceptrepoelki-project/elkirepoalben-mustafalic/automated-cybersecurity-threat-detection-system-with-aws-sagemakerrepoaws-samples/amazon-sagemaker-edge-defect-detection-computer-visionrepoamzn/rheocerosrepomlr-org/mlr3pipelines
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repomlflow/mlflowrepomlr-org/mlr3pipelinesrepoaws/sagemaker-example-notebooks-testingrepoaws/sagemaker-python-sdkrepoelki-project/elkirepoaws-samples/amazon-sagemaker-edge-defect-detection-computer-visionrepoaws/amazon-sagemaker-examplesrepoupgini/upginirepohnminh1005/ds-component-pipelinerepoalben-mustafalic/automated-cybersecurity-threat-detection-system-with-aws-sagemakerrepoamzn/rheocerosrepoSwanHubX/SwanLabrepoSuperCowPowers/workbenchrepoORNL/flowcept