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MLflow

Experiments

Ingests MLflow experiment metadata, tracking names, lifecycle stage, tags, and timestamps from your MLflow tracking server.

Sync Type: Incremental

Prerequisites

Before configuring this input, you need:

1. A running MLflow tracking server — the server must be reachable over HTTPS from Monad. Self-hosted and managed deployments (e.g. Databricks MLflow) are both supported.

2. Credentials for your deployment (if authentication is enabled):

   • None — for OSS MLflow deployments started without the mlflow.server.auth plugin.
   • Basic (username + password) — if the mlflow.server.auth plugin is active on your server.
   • Bearer token — if an OIDC/oauth2-proxy reverse-proxy sits in front of MLflow.

Authentication

MLflow supports three authentication modes. Select the one that matches your deployment:

None

No credentials are required. This is the default for OSS MLflow deployments that were started without the mlflow.server.auth plugin. All API requests are made without an Authorization header.

Basic (username + password)

Used when the mlflow.server.auth plugin is enabled on your MLflow server. Monad sends an HTTP Basic Authorization header with every request.

To create a user (if you administer the server):

Code
mlflow server --app-name basic-auth
# then use the REST admin API or the mlflow CLI to create users

See the MLflow authentication documentation for details.

Bearer token

Used when an OIDC provider or oauth2-proxy reverse-proxy is in front of MLflow and issues short-lived tokens. Provide the token value; Monad sends it as Authorization: Bearer <token> on every request. Rotate the token in Monad when it expires.

Configuration

Settings

Setting	Type	Required	Description
Base URL	string (URL)	Yes	The base URL of the MLflow tracking server. Must use HTTPS. Example: https://mlflow.example.com
Authentication	one of	Yes	Authentication mode and credentials. Choose None, Basic (username + password), or Bearer token — see Authentication above.
Backfill Start Time	string (date-time)	No	ISO 8601 / RFC 3339 date from which to start fetching data on the first run. If omitted, only experiments updated since the connector was created are ingested.
Use Synthetic Data	boolean	No	Generate synthetic demo data instead of connecting to the real MLflow server. Useful for pipeline testing.

Authentication sub-fields

Basic (username + password)

Field	Type	Required	Description
Username	string	Yes	HTTP Basic auth username.
Password	secret	Yes	HTTP Basic auth password.

Bearer token

Field	Type	Required	Description
Bearer Token	secret	Yes	Bearer token issued by an OIDC/oauth2-proxy in front of MLflow.

Setup Walk-through

1. In Monad, create a new MLflow Experiments input.

2. Enter the Base URL of your MLflow tracking server (must start with https://).

3. Select the Authentication mode that matches your MLflow deployment:

   • Choose None for an unauthenticated OSS server.
   • Choose Basic and supply the username and password if mlflow.server.auth is enabled.
   • Choose Bearer token and paste the token if a reverse-proxy handles auth.

4. Optionally set a Backfill Start Time to ingest historical experiments from a specific date.

5. Save the input and attach it to a pipeline. On the first run Monad will page through all experiments updated since the backfill start time (or connector creation) and continue incrementally on subsequent runs.

Troubleshooting

Common Issues

1. Connection refused or SSL errors

   • Confirm the Base URL uses https:// and that the server's TLS certificate is valid. Self-signed certificates are not supported.
   • Check that the MLflow server is reachable from Monad's egress IPs.

2. 401 Unauthorized

   • The authentication mode or credentials are incorrect.
   • For Basic auth, verify the username and password against the mlflow.server.auth user store.
   • For Bearer auth, confirm the token has not expired. Rotate the Bearer Token secret in Monad with a fresh token if needed.

3. 403 Forbidden

   • The user or token has insufficient permissions to call POST /api/2.0/mlflow/experiments/search. Grant the user READ access on experiments in the MLflow auth admin UI.

4. No experiments appearing

   • If Backfill Start Time is set, ensure at least one experiment has a last_update_time on or after that date.
   • Verify the MLflow server has experiments by running mlflow experiments search directly against the server.

Related Articles

• MLflow Tracking documentation
• MLflow authentication
• MLflow REST API — Search Experiments