feat: Feast-MLflow Integration by Vperiodt · Pull Request #6235 · feast-dev/feast

Vperiodt · 2026-04-08T08:02:51Z

What this PR does / why we need it:

final_mlflow_demo.mp4

Auto-logging: Feature retrieval metadata is tagged on the active MLflow run (feast.feature_refs, feast.feature_views, feast.feature_service, feast.entity_count, etc.)
Entity DataFrame archival: Optionally saves the training entity DataFrame as an MLflow artifact (entity_df.parquet) for full reproducibility
Model-to-feature-service resolution: resolve_feature_service_from_model_uri() maps any MLflow model URI back to its Feast feature service enabling serving pipelines to auto-discover which features a model needs
Entity DataFrame reconstruction: get_entity_df_from_mlflow_run() rebuilds the exact entity DataFrame from a past run's artifacts, enabling training reproducibility
Configuration : Controlled entirely via feature_store.yaml under a new mlflow: block

Which issue(s) this PR fixes:

Checks

I've made sure the tests are passing.
My commits are signed off (git commit -s)
My PR title follows conventional commits format

Testing Strategy

Unit tests
Integration tests
Manual tests
Testing is not required for this change

Misc

devin-ai-integration

Devin Review found 1 new potential issue.

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devin-ai-integration · 2026-04-15T11:01:00Z

+                    with tempfile.TemporaryDirectory() as tmp_dir:
+                        path = os.path.join(tmp_dir, "entity_df.parquet")
+                        entity_df.to_parquet(path, index=False)
+                        mlflow.log_artifact(path)


🟡 Mixing global mlflow.log_artifact() with explicit-URI client causes artifact/metadata to target different servers

In _auto_log_entity_df_info, tags and params are logged via a locally-created MlflowClient(tracking_uri=tracking_uri) (lines 304, 309-318), but the entity DataFrame artifact is uploaded via the global mlflow.log_artifact(path) (line 327). The global function uses the tracking URI set by mlflow.set_tracking_uri(), not the explicit tracking_uri from the config. If the global tracking URI is changed by another library in the process, or if _init_mlflow_tracking failed silently (caught by except Exception at sdk/python/feast/feature_store.py:249), the artifact would be uploaded to a different server than where the tags/params were logged, splitting metadata across two servers.

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devin-ai-integration

Devin Review found 2 new potential issues.

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devin-ai-integration · 2026-04-16T12:54:56Z

+        try:
+            import mlflow
+
+            tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"


🔴 UI server ignores MLFLOW_TRACKING_URI env var, falls back to hardcoded localhost

In ui_server.py, both the /api/mlflow-runs and /api/mlflow-feature-models endpoints resolve the MLflow tracking URI using mlflow_cfg.tracking_uri or "http://127.0.0.1:5000". This reads the raw tracking_uri field from the config and falls back to a hardcoded localhost URL, completely bypassing the MLFLOW_TRACKING_URI environment variable. In contrast, the rest of the codebase (feature_store.py:243, feature_store.py:325, feature_store.py:1696, feature_store.py:2885) correctly calls mlflow_cfg.get_tracking_uri() which checks the env var via sdk/python/feast/mlflow_integration/config.py:19-29. When a user sets MLFLOW_TRACKING_URI without setting tracking_uri in YAML (which is a very common deployment pattern, and documented in the PR's own docs at docs/reference/mlflow.md:51), the UI endpoints will incorrectly connect to http://127.0.0.1:5000 instead of the env-var-specified server.

Suggested change

tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"

tracking_uri = mlflow_cfg.get_tracking_uri() or "http://127.0.0.1:5000"

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devin-ai-integration · 2026-04-16T12:54:57Z

+        try:
+            import mlflow
+
+            tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"


🔴 Second UI endpoint also ignores MLFLOW_TRACKING_URI env var

Same issue as in the /api/mlflow-runs endpoint: the /api/mlflow-feature-models endpoint at sdk/python/feast/ui_server.py:234 uses mlflow_cfg.tracking_uri or "http://127.0.0.1:5000" instead of mlflow_cfg.get_tracking_uri(), causing the MLFLOW_TRACKING_URI environment variable to be ignored.

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devin-ai-integration

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devin-ai-integration · 2026-04-20T09:29:23Z

            )
+
+            # Emit MLflow event for materialization (Phase 7)
+            _mat_duration = time.monotonic() - _retrieval_start if '_retrieval_start' in dir() else 0


🟡 Undefined _retrieval_start variable in materialize_incremental causes bogus duration or NameError

In materialize_incremental, the code at line 2115 references _retrieval_start which is never defined in that method scope. The expression '_retrieval_start' in dir() checks local variable names but dir() without arguments returns module-level names, not local variables -- so the check is unreliable. If _retrieval_start is not in the returned list, _mat_duration defaults to 0 (acceptable but meaningless). However, if it IS in the name list (e.g., a module-level _retrieval_start existed from another context), it would attempt time.monotonic() - _retrieval_start on a potentially unrelated value, resulting in a wrong duration or a NameError/TypeError at runtime.

Suggested change

_mat_duration = time.monotonic() - _retrieval_start if '_retrieval_start' in dir() else 0

_mat_duration = 0

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devin-ai-integration · 2026-04-20T09:29:24Z

+        if tracking_uri:
+            mlflow.set_tracking_uri(tracking_uri)
+
+        experiment_name = f"{project}{ops_experiment_suffix}"
+        mlflow.set_experiment(experiment_name)


🔴 log_apply_to_mlflow and log_materialize_to_mlflow mutate global MLflow tracking URI and experiment, corrupting concurrent user runs

Both log_apply_to_mlflow (sdk/python/feast/mlflow_integration/logger.py:185-189) and log_materialize_to_mlflow (sdk/python/feast/mlflow_integration/logger.py:251-255) call mlflow.set_tracking_uri(tracking_uri) and mlflow.set_experiment(experiment_name) globally. These mutate process-wide global state. If a user has an active MLflow run in a different experiment (e.g. during feast apply inside a training script), these calls redirect subsequent MLflow operations to the ops experiment. While the functions try to restore mlflow.set_experiment(project) afterward (line 224, 274), the tracking URI is never restored. In the exception path, if mlflow.start_run() raises (line 202/258), the experiment remains set to the ops experiment. This corrupts any concurrent or subsequent user operations in the same process.

Prompt for agents

The functions log_apply_to_mlflow and log_materialize_to_mlflow in sdk/python/feast/mlflow_integration/logger.py mutate process-wide global state by calling mlflow.set_tracking_uri() and mlflow.set_experiment(). This corrupts any concurrent user MLflow runs in the same process. The fix should save and restore the original tracking URI and experiment before/after the ops logging, or preferably use the MlflowClient API exclusively (which already takes tracking_uri as a parameter) instead of the global mlflow module-level functions. The MlflowClient.create_run() method allows creating runs in specific experiments without mutating global state. This pattern is already used in log_feature_retrieval_to_mlflow which correctly uses client = mlflow.MlflowClient(tracking_uri=tracking_uri) without touching global state.

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devin-ai-integration · 2026-04-20T09:29:25Z

+    def mlflow(self):
+        """Get the MLflow configuration."""
+        if not self._mlflow:
+            if isinstance(self.mlflow_config, Dict):
+                from feast.mlflow_integration.config import MlflowConfig
+
+                self._mlflow = MlflowConfig(**self.mlflow_config)
+            elif self.mlflow_config:
+                self._mlflow = self.mlflow_config
+        return self._mlflow


🟡 mlflow property returns falsy None on subsequent calls when MlflowConfig(enabled=False) is set

The mlflow property in RepoConfig (sdk/python/feast/repo_config.py:497-506) uses if not self._mlflow: as the guard. When the config is a valid MlflowConfig object with enabled=False, self._mlflow will be a truthy MlflowConfig instance (Pydantic models are truthy), so this works correctly on first access. However, if mlflow_config is None (the default), self._mlflow stays None forever and the property returns None -- which is fine. The real issue is: if mlflow_config is an empty dict {}, MlflowConfig(**{}) creates a valid config with enabled=False, and self._mlflow becomes truthy. But if mlflow_config is set to some falsy-like Pydantic object (unlikely but possible with custom subclasses), the if not self._mlflow guard would re-create it every time. This is the same pattern as openlineage property so it's consistent, but not actually a bug for standard usage.

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franciscojavierarceo · 2026-04-20T13:42:35Z

+- **Online feature retrieval** -- `get_online_features()` tags the run with the same metadata
+- **Entity DataFrame archival** -- optionally saves the training entity DataFrame as an MLflow artifact for full reproducibility
+- **Execution context tagging** -- tags runs with where they ran (workbench, KFP pipeline, feature server, or standalone)
+- **Operation logging** -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment


not sure this is actually useful

actually, this is not updated one, thought of this one for backtracking from mllfow run to the workbench experiment but now it is omitted

franciscojavierarceo · 2026-04-20T13:42:52Z

+- **Entity DataFrame archival** -- optionally saves the training entity DataFrame as an MLflow artifact for full reproducibility
+- **Execution context tagging** -- tags runs with where they ran (workbench, KFP pipeline, feature server, or standalone)
+- **Operation logging** -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment
+- **Model-to-Feature resolution** -- map any MLflow model URI back to its Feast feature service


franciscojavierarceo · 2026-04-20T13:43:00Z

+- **Execution context tagging** -- tags runs with where they ran (workbench, KFP pipeline, feature server, or standalone)
+- **Operation logging** -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment
+- **Model-to-Feature resolution** -- map any MLflow model URI back to its Feast feature service
+- **Training reproducibility** -- reconstruct the exact entity DataFrame from a past MLflow run


franciscojavierarceo · 2026-04-20T13:43:06Z

+- **Operation logging** -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment
+- **Model-to-Feature resolution** -- map any MLflow model URI back to its Feast feature service
+- **Training reproducibility** -- reconstruct the exact entity DataFrame from a past MLflow run
+- **Training-to-prediction linkage** -- `FeastMlflowClient.load_model()` links prediction runs back to their training runs


franciscojavierarceo · 2026-04-20T13:43:17Z

+- **Model-to-Feature resolution** -- map any MLflow model URI back to its Feast feature service
+- **Training reproducibility** -- reconstruct the exact entity DataFrame from a past MLflow run
+- **Training-to-prediction linkage** -- `FeastMlflowClient.load_model()` links prediction runs back to their training runs
+- **Feast MLflow Client** -- a thin wrapper that eliminates direct `import mlflow` in user code


makes sense

franciscojavierarceo · 2026-04-20T13:46:33Z

+    _consecutive_failures = 0
+
+
+def log_feature_retrieval_to_mlflow(


i guess if we have this enabled why do we need to even have the wrapper? can't we full handle this for the user?

I guess my question is, can't we just hide all of the mlflow client usage to the user?

the auto-logging in logger.py fully handled for users who already use mlflow.start_run() or client.start_run() considering our FeastMlflowClient wrapper which is for users who want to avoid import mlflow entirely and get additional Feast-specific behavior on top ex: logging a model, registering it, loading it for inference etc soFeastMlflowClient is for users who want the extra lineage features without touching MLflow directly

I would rather push all these functions to integration client as everything here is a part of mlflow feast integration?

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jyejare

Comments inline:

We also need to enable mlflow configuratins from feast operator else admin has to do it explicitly.
Also, we need to remove duplicate mechanisms for initiating/using mlflow client and we should provide a clean way that is store.mlflow whenever possible.

jyejare · 2026-04-22T08:57:55Z

+2. `MLFLOW_TRACKING_URI` environment variable
+3. MLflow's default (`./mlruns` local directory)
+
+This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead.


Suggested change

This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead.

This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead or it would be pulled from /.mlruns automatically when both are not set.

wdyt ?

jyejare · 2026-04-22T09:01:16Z

+
+| Artifact | Description |
+|----------|-------------|
+| `required_features.json` | JSON list of feature references the model was trained on |


Could we rename to relate it with feast better and so that it would be self describing in MLFLow ecosystem, something like:

required_feast_features.json

OR

feast_features.json

jyejare · 2026-04-22T09:05:10Z

+With the configuration above, feature metadata is logged whenever there is an active MLflow run:
+
+```python
+import mlflow


I think explicit import is no more required as feast module gives you the ability to:

store.mlflow.start_run(....

jyejare · 2026-04-22T09:07:57Z

+from feast import FeatureStore
+
+store = FeatureStore(".")
+client = store.get_mlflow_client()


Even this is changed now. Lets keep only store.mlflow. examples.

jyejare · 2026-04-22T10:58:56Z

+| Condition | Error |
+|-----------|-------|
+| No `feature_store.yaml` in cwd and no store created | `RuntimeError` with guidance to call `feast.mlflow.init(store)` |
+| `mlflow.enabled` is `false` or missing | `RuntimeError` with guidance to set `mlflow.enabled=true` |


Even if user wants to set it to false ?

jyejare · 2026-04-22T12:00:10Z

+            invalid, resolution fails, or validation against the store fails.
+    """
+    try:
+        import mlflow


Same here. Create a class and initiate in integration object/class that takes store object as param.

jyejare · 2026-04-22T12:07:42Z

+            if mlflow_cfg is None or not mlflow_cfg.enabled:
+                return
+
+            import mlflow


The store object is available to you here as self, so why not :

self.mlflow.set_tracking_uri(....

jyejare · 2026-04-22T13:12:01Z

+        client = MlflowClient(tracking_uri=tracking_uri)
+        refs = ["z_view:f1", "a_view:f2", "z_view:f3", "a_view:f4"]
+
+        with mlflow.start_run() as run:


The tests should also be focusing only on store.mlflow... culture.

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Vperiodt force-pushed the feast-mlflow branch from 0e6296b to a63d529 Compare April 9, 2026 12:05

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Vperiodt changed the title ~~Feast-MLflow Integration~~ feat: Feast-MLflow Integration Apr 15, 2026

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Vperiodt added 4 commits April 22, 2026 14:17

mlflow-feast integration

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refine MLflow integration, UI runs endpoint, and lockfiles

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fix issues

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handle fallback

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added model to features table

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mlflowclient wrapper

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update feastMlflowClient

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jyejare reviewed Apr 22, 2026

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Vperiodt and others added 2 commits April 27, 2026 13:23

Merge branch 'feast-dev:master' into feast-mlflow

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changing init

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Vperiodt added 2 commits April 27, 2026 16:52

fixing docs

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Merge branch 'feast-dev:master' into feast-mlflow

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update format

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fix-test

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	tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"
	tracking_uri = mlflow_cfg.get_tracking_uri() or "http://127.0.0.1:5000"

	_mat_duration = time.monotonic() - _retrieval_start if '_retrieval_start' in dir() else 0
	_mat_duration = 0

		_consecutive_failures = 0


		def log_feature_retrieval_to_mlflow(

	This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead.
	This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead or it would be pulled from /.mlruns automatically when both are not set.

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What this PR does / why we need it:

Which issue(s) this PR fixes:

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