> For the complete documentation index, see [llms.txt](https://docs.feast.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feast.dev/master/how-to-guides/feast-on-kubernetes/feast-operator/08-mlflow-integration.md). # 8 — MLflow Integration The operator auto-discovers MLflow on RHOAI/ODH clusters and enables experiment tracking for every FeatureStore deployment. When the MLflow operator is present and healthy, Feast pods receive MLflow configuration automatically — no manual YAML editing required. *** ## Auto-discovery The operator lists all `MLflow` CRs (`mlflow.opendatahub.io/v1`) in the cluster and uses the first one with an `Available=True` or `Ready=True` condition. When found, it populates `tracking_uri` from `status.address.url` and `ui_url` from `status.url`. If the MLflow CR does not report conditions (older operator versions), auto-discovery will not activate. Set `trackingUri` explicitly in that case. > **No MLflow?** The FeatureStore stays Ready. Non-MLflow FeatureViews and all other Feast services are completely unaffected. *** ## FeatureStore CR configuration ### Auto-enabled (default when MLflow is present) No `spec.mlflow` needed. The operator auto-enables when an Available MLflow CR is detected: ```yaml apiVersion: feast.dev/v1 kind: FeatureStore metadata: name: my-store spec: feastProject: my_project services: onlineStore: {} registry: {} ui: {} ``` ### Explicit configuration Override defaults or enable additional features: ```yaml apiVersion: feast.dev/v1 kind: FeatureStore metadata: name: my-store spec: feastProject: my_project services: onlineStore: {} registry: {} ui: {} mlflow: enabled: true trackingUri: "https://custom-mlflow.example.com:8443" uiUrl: "https://dashboard.example.com/mlflow" trackingAuth: "kubernetes-namespaced" autoLog: true autoLogEntityDf: true entityDfMaxRows: 50000 logOperations: true opsExperimentSuffix: "-feast-ops" ``` ### Opt-out Disable MLflow even when the MLflow operator is present: ```yaml spec: mlflow: enabled: false ``` *** ## Field reference | Field | Type | Default | Description | | --------------------- | ------------------ | ------------------------- | --------------------------------------------------------------------------- | | `enabled` | bool | auto-detected | Master switch for MLflow integration | | `trackingUri` | string | auto-discovered | MLflow tracking server URI (in-cluster, from `status.address.url`) | | `uiUrl` | string | auto-discovered | Browser-reachable MLflow URL for Feast UI lineage links (from `status.url`) | | `trackingAuth` | \*string | `"kubernetes-namespaced"` | Auth method for Feast pods calling MLflow | | `autoLog` | \*bool | `true` | Auto-log feature metadata on every retrieval | | `autoLogEntityDf` | \*bool | `false` | Save entity DataFrame as artifact | | `entityDfMaxRows` | \*int32 | `100000` | Skip artifact for large DataFrames | | `logOperations` | \*bool | `false` | Log `feast apply` / `materialize` to ops experiment | | `opsExperimentSuffix` | \*string | `"-feast-ops"` | Ops experiment name suffix | | `extraConfig` | map\[string]string | — | Additional YAML fields (coerced to native types) | *** ## Authentication The operator injects `MLFLOW_TRACKING_AUTH` into all Feast pod containers. The MLflow Python client's auth plugin system uses this env var to attach credentials to tracking server requests. | `trackingAuth` value | Behavior | | ----------------------------------- | ------------------------------------------------------------------------------------- | | `"kubernetes-namespaced"` (default) | SA token + `X-MLFLOW-WORKSPACE: ` header. Multi-tenant on RHOAI. | | `"kubernetes"` | SA token only. Single-tenant setups. | | `"basic"` | HTTP Basic auth via `MLFLOW_TRACKING_USERNAME` / `MLFLOW_TRACKING_PASSWORD` env vars. | | `"bearer"` | Static bearer token from `MLFLOW_TRACKING_TOKEN` env var. | | `""` (empty string) | No auth header. Local dev or unprotected MLflow. | No Kubernetes RoleBinding is needed for MLflow tracking API access. The MLflow server validates the SA token directly via TokenReview. *** ## Tracking URI resolution order 1. Explicit `trackingUri` in the FeatureStore CR 2. Auto-discovered from MLflow CR `status.address.url` (first Available/Ready CR) 3. `MLFLOW_TRACKING_URI` environment variable (on workbench pods, injected by the MLflow operator) 4. MLflow default (`./mlruns`) *** ## UI URL resolution order Used for browser hyperlinks in Feast UI lineage panels: 1. Explicit `uiUrl` in the FeatureStore CR 2. `MLFLOW_UI_URL` environment variable 3. Auto-discovered from MLflow CR `status.url` (external gateway route) 4. Falls back to `trackingUri` (works for local dev) *** ## Graceful degradation | Scenario | Behavior | | -------------------------------- | ---------------------------------------------------- | | MLflow operator not installed | No `mlflow` block in YAML; FeatureStore stays Ready | | MLflow CR exists but not Ready | Discovery returns empty; MLflow stays off | | Tracking URI becomes unreachable | SDK logs a warning; feature retrieval is not blocked | | `spec.mlflow.enabled: false` | MLflow integration explicitly disabled | *** ## Workbench usage In a RHOAI workbench notebook connected to the FeatureStore: ```python from feast import FeatureStore store = FeatureStore(...) # from mounted client config with store.mlflow.start_run(run_name="training"): df = store.get_historical_features( entity_df=entity_df, features=["driver_stats:conv_rate", "driver_stats:acc_rate"], ).to_df() model = train(df) store.mlflow.log_model(model, "model") ``` > **Dependency**: the Feast image must include `feast[mlflow]` (`mlflow` or `mlflow-skinny`). *** ## RBAC permissions The operator needs `get`, `list`, `watch` on `mlflows` in the `mlflow.opendatahub.io` API group. This is included in the default operator ClusterRole. ```yaml - apiGroups: - mlflow.opendatahub.io resources: - mlflows verbs: - get - list - watch ``` *** ## See also * [API field reference — `MlflowConfig`](https://github.com/feast-dev/feast/blob/stable/infra/feast-operator/docs/api/markdown/ref.md) * [MLflow DataSource reference](/master/reference/mlflow.md) * [Guide 5 — Security](/master/how-to-guides/feast-on-kubernetes/feast-operator/05-security.md) (RBAC / OIDC auth) * [Guide 7 — OpenLineage & Materialization](/master/how-to-guides/feast-on-kubernetes/feast-operator/07-openlineage-and-materialization.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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