sagemaker-mlflow

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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".

10installs

Sourcemlflow/skills

Added on2026-07-31

NPX Install

npx skill4agent add mlflow/skills sagemaker-mlflow

SKILL.md Content

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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
```
.

sagemaker-mlflow

NPX Install

Tags

SKILL.md Content

Connect to SageMaker MLflow

Step 1: Preconditions

Step 2: Install the plugin

Step 3: Select the resource ARN

Step 4: Verify

Troubleshooting