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Streamline external access to Amazon SageMaker MLflow using a REST API proxy | Amazon Web Services
In this post, we demonstrate how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This solution is for organizations undergoing cloud transformation who want to preserve their existing ML workflows while adopting cloud-native services.
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Streamline external access to Amazon SageMaker MLflow using a REST API proxy | Amazon Web Services
In this post, we demonstrate how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This…
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Build a custom portal with embedded Amazon SageMaker AI MLflow Apps | Amazon Web Services
In this post, you learn how to build a custom portal with embedded SageMaker AI MLflow Apps UI. You walk through the architecture pattern behind a React front end paired with a…