sagemaker-mlflow
AWS Plugin for MLflow with SageMaker
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS credentials available via boto3 (environment variables, shared config, instance role, or explicit session).
- Low friction installation with minimal dependencies—depends only on boto3 and mlflow-skinny by default.
- Active maintenance with a recent release and no known vulnerabilities.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial use and modification with minimal restrictions.
last release 2026-06-27 (48 days) · last repo commit 2026-07-06 · 26 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,351,520 downloads/mo, #2,651 on PyPI
Alternatives
Verify before relying
pip install sagemaker-mlflow
import boto3
import mlflow
import sagemaker_mlflow
custom = boto3.Session(profile_name="my-profile")
with sagemaker_mlflow.use_session(custom):
mlflow.MlflowClient().search_experiments(max_results=1)- Whether the plugin works with all MLflow versions listed in tox.ini or only specific ones.
- Performance overhead of SigV4 signing on high-volume MLflow tracking requests.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
sagemaker-mlflow on PyPI
Before you install
Low friction installation with minimal dependencies—depends only on boto3 and mlflow-skinny by default. Active maintenance with a recent release and no known vulnerabilities.
Requires AWS credentials available via boto3 (environment variables, shared config, instance role, or explicit session).
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial use and modification with minimal restrictions.
Quickstart
pip install sagemaker-mlflow
import boto3
import mlflow
import sagemaker_mlflow
custom = boto3.Session(profile_name="my-profile")
with sagemaker_mlflow.use_session(custom):
mlflow.MlflowClient().search_experiments(max_results=1)Verify before relying
- Whether the plugin works with all MLflow versions listed in tox.ini or only specific ones.
- Performance overhead of SigV4 signing on high-volume MLflow tracking requests.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesboto3mlflow-skinny |
| Maintenance | Actively maintained 48 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,351,520 / month, #2,651 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: sagemaker_mlflow-0.5.0-py3-none-any.whl
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See also sagemaker-train · sagemaker-experiments · requests-aws-sign · azureml-mlflow · mlflow · sagemaker-training · sagemaker · mlflow-tracing · requests-sigv4 · aws-sdk-signers