How do I install the Sagemaker Mlflow skill?

Run npx skillmds@latest add mlflow/sagemaker-mlflow in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Sagemaker Mlflow safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Sagemaker Mlflow?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Sagemaker Mlflow free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Sagemaker Mlflow?

mlflow (@mlflow) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Sagemaker Mlflow by mlflow — Agent Skill

Name: Sagemaker Mlflow
Rating: 4.666666666666667 (3 reviews)
Author: mlflow

Sagemaker Mlflow

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".

mlflow 2caa387 3 files · 7.0 KB Updated Sep 9, 2026

File contents

Connect to SageMaker MLflow

Establishes the connection to SageMaker Managed MLflow (the sagemaker-mlflow plugin + an ARN tracking URI + AWS credentials), then hands off to the requested MLflow skill (instrumenting-with-mlflow-tracing, agent-evaluation, retrieving-mlflow-traces, querying-mlflow-metrics, etc.), which inherit the connection via MLFLOW_TRACKING_URI.

Requires Python >= 3.10 (mlflow >= 3.8). Validate and report the fix — never auto-install 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/verify_connection.py also checks this, via boto3.)

Step 2: Install the plugin

pip install sagemaker-mlflow

Required even if MLFLOW_TRACKING_URI is already an ARN — without it, mlflow.set_tracking_uri() fails with UnsupportedModelRegistryStoreURIException.

Step 3: Select the resource ARN

If the user already provided an ARN (or MLFLOW_TRACKING_URI is set to arn:aws:sagemaker:...), use it as-is.
Otherwise, discover it in the user's region:
```
python scripts/discover_arns.py
```
If exactly one is returned, use it; if several, list name/status/ARN and ask the user which to use. Both mlflow-app and mlflow-tracking-server ARNs work; the ARN is also shown on the resource's detail page in the SageMaker Studio console.

Then export it (the ARN is region-bound — a cross-region ARN will not authenticate):

export MLFLOW_TRACKING_URI="<arn>"    # mlflow-app/<id> or mlflow-tracking-server/<name>
export AWS_DEFAULT_REGION="<region>"  # AWS credentials resolved via the default chain (SigV4)

Step 4: Verify

python scripts/verify_connection.py "$MLFLOW_TRACKING_URI"

Checks all three prerequisites (plugin / credentials / ARN connectivity) and exits non-zero with a remediation hint on the first failure. Do not proceed until it passes.

After the connection verifies, hand off to the requested MLflow skill — it inherits the connected environment via MLFLOW_TRACKING_URI.

Troubleshooting

UnsupportedModelRegistryStoreURIException — the sagemaker-mlflow plugin is not installed; pip install sagemaker-mlflow, then re-run scripts/verify_connection.py.
AccessDenied / 403 — credentials are missing/expired, or the principal lacks MLflow permissions on the ARN. Check that credentials resolve, grant sagemaker-mlflow / sagemaker permissions, and confirm the ARN region matches AWS_DEFAULT_REGION.

---name: sagemaker-mlflowdescription: 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".--- # Connect to SageMaker MLflow Establishes the connection to SageMaker Managed MLflow (the `sagemaker-mlflow` plugin + an ARN tracking URI + AWS credentials), then hands off to the requested MLflow skill (instrumenting-with-mlflow-tracing, agent-evaluation, retrieving-mlflow-traces, querying-mlflow-metrics, etc.), which inherit the connection via `MLFLOW_TRACKING_URI`. Requires **Python >= 3.10** (mlflow >= 3.8). Validate and report the fix — never auto-install 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/verify_connection.py` also checks this, via boto3.) ## Step 2: Install the plugin ```bash pip install sagemaker-mlflow ``` Required even if `MLFLOW_TRACKING_URI` is already an ARN — without it, `mlflow.set_tracking_uri()` fails with `UnsupportedModelRegistryStoreURIException`. ## Step 3: Select the resource ARN - **If the user already provided an ARN** (or `MLFLOW_TRACKING_URI` is set to `arn:aws:sagemaker:...`), use it as-is. - **Otherwise, discover it** in the user's region: ```bash python scripts/discover_arns.py ``` If exactly one is returned, use it; if several, list name/status/ARN and ask the user which to use. Both `mlflow-app` and `mlflow-tracking-server` ARNs work; the ARN is also shown on the resource's detail page in the SageMaker Studio console. Then export it (the ARN is region-bound — a cross-region ARN will not authenticate): ```bash export MLFLOW_TRACKING_URI="<arn>" # mlflow-app/<id> or mlflow-tracking-server/<name> export AWS_DEFAULT_REGION="<region>" # AWS credentials resolved via the default chain (SigV4) ``` ## Step 4: Verify ```bash python scripts/verify_connection.py "$MLFLOW_TRACKING_URI" ``` Checks all three prerequisites (plugin / credentials / ARN connectivity) and exits non-zero with a remediation hint on the first failure. Do not proceed until it passes. After the connection verifies, hand off to the requested MLflow skill — it inherits the connected environment via `MLFLOW_TRACKING_URI`. ## Troubleshooting - `UnsupportedModelRegistryStoreURIException` — the `sagemaker-mlflow` plugin is not installed; `pip install sagemaker-mlflow`, then re-run `scripts/verify_connection.py`. - `AccessDenied` / `403` — credentials are missing/expired, or the principal lacks MLflow permissions on the ARN. Check that credentials resolve, grant `sagemaker-mlflow` / `sagemaker` permissions, and confirm the ARN region matches `AWS_DEFAULT_REGION`.

Frequently asked questions