{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Integrates MLflow with Amazon SageMaker by signing requests with AWS IAM credentials and enabling model registration to the SageMaker Model Registry.","skillfed_tags":["aws-integration","mlflow-plugin","model-registry"],"use_cases":["Run MLflow experiment tracking on SageMaker with automatic AWS IAM authentication instead of managing separate credentials.","Register trained models to the SageMaker Model Registry directly from MLflow without manual AWS API calls.","Use different AWS profiles or per-tenant credentials in a shared process by injecting custom boto3 sessions.","Integrate MLflow into SageMaker training jobs or SageMaker Pipelines with minimal configuration overhead.","Sign MLflow requests with AWS IAM for audit trails and fine-grained access control in enterprise environments."],"what_it_does":"This is an AWS plugin for MLflow that bridges MLflow's experiment tracking and model registry with Amazon SageMaker. It generates Signature V4 headers for outgoing requests, handles AWS IAM authentication and authorization, and enables model registration directly to the SageMaker Model Registry. The plugin works as an MLflow entry point and signs requests using credentials from the boto3 default chain (environment variables, shared config, instance role) or a custom boto3.Session you provide.\n\nThe plugin is lightweight by default, depending only on boto3 and mlflow-skinny, with an optional [full] extra for the complete MLflow package. It supports Python 3.8 through 3.14 and is actively maintained. You can inject a custom AWS session via a context manager (use_session) or set a default (set_session) without mutating environment variables, making it suitable for multi-tenant or multi-profile scenarios.","worth_installing":"Yes. This plugin is worth installing if you use MLflow with SageMaker and need AWS IAM-based authentication. It has low install friction, active maintenance, no known vulnerabilities, and a permissive license. The tight integration with boto3 and support for custom sessions makes it practical for both simple and multi-tenant deployments."},"id":"sagemaker-mlflow","links":{"html":"https://skillfed.io/packages/sagemaker-mlflow","md":"https://skillfed.io/packages/sagemaker-mlflow.md","pypi":"https://pypi.org/project/sagemaker-mlflow/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-27","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-mlflow","python_support":"supports_current","summary":"AWS Plugin for MLflow with SageMaker"},"popularity":{"monthly_downloads":3351520,"position":2651,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}