# sagemaker-mlflow

> Claude/Codex skill by [@mlflow](https://omgskills.com/library/mlflow/index.md) · 82 stars · Updated 2026-10-23

Connect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an MLflow backend, then hand off to the other MLflow skills. Triggers on a SageMaker MLflow ARN (arn:aws:sagemaker:...:mlflow-app/... or arn:aws:sagemaker:...:mlflow-tracking-server/...) or "SageMaker Managed MLflow".

## Install

```sh
git clone https://github.com/mlflow/skills /tmp/skills && ln -s /tmp/skills/sagemaker-mlflow ~/.claude/skills/sagemaker-mlflow
```

## From README

Connect to SageMaker MLflow Connect using the sagemaker-mlflow plugin, an ARN tracking URI and AWS credentials. Other MLflow skills inherit the connection through MLFLOWTRACKINGURI. Requires Python \>= 3.10 (mlflow \>= 3.8). Validate and report the fix — never auto- the plugin or acquire credentials (mirrors the repo's Databricks pattern). Step 1: Preconditions python --version -\> must be 3.10+. If older, STOP and tell the user. Confirm AWS credentials resolve (env vars, aws configure / SSO, or an IAM role). If they error (ExpiredToken / no creds), STOP and ask the user to configure them. (scripts/verifyconnection.py also checks this, via boto3.) Step 2: Check the plugin If missing, stop and give the user this command: Required even if MLFLOWTRACKINGURI is already an ARN — without it, mlflow.settrackinguri() fails with UnsupportedModelRegistryStoreURIException.

## Links

- HTML page: https://omgskills.com/library/mlflow/skills/sagemaker-mlflow/
- GitHub: https://github.com/mlflow/skills
- Author: [@mlflow](https://omgskills.com/library/mlflow/index.md)

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