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

Native MLflow integration for Feast that provides automatic feature lineage tracking alongside ML experiments. When enabled via feature_store.yaml, every feature retrieval is logged to the active MLflow run.

Key Capabilities

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: store.mlflow.resolve_features() 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: store.mlflow.get_training_entity_df() rebuilds the exact entity DataFrame from a past run's artifacts, enabling training reproducibility
Model lifecycle lineage: log_model auto-attaches feast_features.json, register_model propagates feast.feature_service to model versions, load_model links prediction runs to training runs
Operations audit trail: feast apply and feast materialize logged to a dedicated {project}-feast-ops experiment
Dataset tracking: log_training_dataset() logs training DataFrames as MLflow dataset inputs
Two access patterns: store.mlflow (explicit, multi-store safe) and feast.mlflow (drop-in replacement for import mlflow)
Configuration: Controlled entirely via feature_store.yaml under a new mlflow: block

Demo

Full end-to-end demo notebook with all capabilities: integration-example

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.

View 19 additional findings in Devin Review.

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.

View 20 additional findings in Devin Review.

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

Devin Review found 3 new potential issues.

View 22 additional findings in Devin Review.

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?

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.

ntkathole · 2026-05-12T05:49:34Z

+            ops_suffix = mlflow_cfg.ops_experiment_suffix
+
+            effective_uri = self._tracking_uri or self._mlflow.get_tracking_uri()
+            client = self._mlflow.MlflowClient(tracking_uri=effective_uri)


instead of creating new client everytime, can we initialise once and reuse ?

ntkathole · 2026-05-12T05:57:42Z

        # Initialize feature service cache for performance optimization
        self._feature_service_cache = {}

+        # Cache for _resolve_feature_service_name lookups


the MLflow auto-log could resolve to a deleted or renamed feature service. _fs_name_cache and _fs_name_index caches (used by _resolve_feature_service_name) are never cleared when refresh_registry() or _clear_feature_service_cache() is called

ntkathole · 2026-05-12T06:01:22Z

+        self._fs_name_cache = {}
+        self._fs_name_index_ts = time.monotonic()
+
+    def _resolve_feature_service_name(self, feature_refs: List[str]) -> Optional[str]:


can't we use existing _feature_service_cache from FeatureStore?

_feature_service_cache lookup is : service_name → [feature_refs]and is populated lazily on demand
_fs_name_index lookup is reverse {feature_refs} → service_name, populated from all registry services needed for subset matching when users pass raw refs instead of a FeatureService

ntkathole · 2026-05-12T06:02:10Z


+        try:
+            if self.mlflow is not None and self.config.mlflow.auto_log:
+                import asyncio


already imported at top

ntkathole · 2026-05-12T06:05:48Z

+        self._fs_name_index_ts: float = -self._FS_NAME_INDEX_TTL_SECONDS
+
+        self._mlflow_client: Optional[Any] = None
+        self._init_mlflow()


I think better to do lazy loading here, first check the config and then load module.

ntkathole · 2026-05-12T06:07:05Z

+        self._default_experiment = store.config.project
+
+        if self._tracking_uri:
+            _mlflow_mod.set_tracking_uri(self._tracking_uri)


remove set_tracking_uri() from __init__ and instead pass tracking_uri explicitly when needed (the MlflowClient already receives it)

ntkathole · 2026-05-12T06:09:16Z

        )

+    @app.get("/api/mlflow-runs")
+    def get_mlflow_runs(max_results: int = 50):


should this be a registry API? Not a blocker for this PR, but architecture wise, registry is source of truth

Yes, will add this one will open to other benefits too !

@Vperiodt going to handle this in this PR or different one?

yess, will address this in a follow up PR

franciscojavierarceo · 2026-05-13T11:53:05Z

linter is failing

Vperiodt · 2026-05-13T15:07:39Z

linter is failing

fixed

ntkathole · 2026-05-14T07:22:05Z

include mlflow here in ci dependencies

ntkathole · 2026-05-14T07:31:32Z

        <EuiFlexItem>
          <EuiStat title={`${numOfFs}`} description="Consuming Services" />
        </EuiFlexItem>
+        <EuiFlexItem>


This should be conditionally rendered, users who don't use MLflow will wonder what these stats mean and why they're there

ntkathole · 2026-05-14T07:32:24Z

+  const usage: FeatureUsageEntry | undefined =
+    data.feature_usage?.[featureViewName];
+
+  if (!usage || usage.run_count === 0) {


same here, if mlflow is not configured, this needs to be hidden

ntkathole · 2026-05-14T07:34:26Z

@Vperiodt Guard the UI components behind a check on whether MLflow data is available. The simplest approach is to have the backend return a mlflow_enabled: false flag in the API responses (or a new lightweight /api/mlflow-status endpoint), and conditionally render MLflow components only when enabled.

Vperiodt · 2026-05-14T07:43:20Z

@Vperiodt Guard the UI components behind a check on whether MLflow data is available. The simplest approach is to have the backend return a mlflow_enabled: false flag in the API responses (or a new lightweight /api/mlflow-status endpoint), and conditionally render MLflow components only when enabled.

hello ! @ntkathole, Addressed all reviews

ntkathole · 2026-05-15T15:08:14Z

@Vperiodt need to update pixi.lock file, also please rebase

Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED refine MLflow integration, UI runs endpoint, and lockfiles Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fix issues Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED handle fallback Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED added model to features table Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED address CI Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED add docs Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fix-lint Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update docs Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update-logic Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED add-integration-test Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED mlflowclient wrapper Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED address-reviews Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update docs Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fix-lint Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fix-auto-log Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update feastMlflowClient Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED changing init Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fixing docs Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED remove doc Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update format Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fix-test Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update docs Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED addressed reviews Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED fix: update .secrets.baseline to remove pragma-allowlisted entry Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED ui-fallback Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED update requirements Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED

Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED

ntkathole

Looks good to me! Thank you @Vperiodt , This is great

franciscojavierarceo · 2026-05-21T10:21:23Z

lgtm

# [0.64.0](v0.63.0...v0.64.0) (2026-06-13) ### Bug Fixes * Add async_supported property to RedisOnlineStore ([9b088fe](9b088fe)) * Add missing feast init templates to operator CRD and enhance persistence documentation ([1941d4d](1941d4d)) * Allow to publish from reference branch ([5458ec8](5458ec8)) * API calls list ([4203eb7](4203eb7)) * **bigquery:** Enable list inference for parquet loads in offline_write_batch ([9243497](9243497)), closes [#5845](#5845) * Bump grpcio dependencies ([07b4782](07b4782)) * **compute-engine/local:** Honor field_mapping on join keys in dedup + join nodes ([#6395](#6395)) ([bd01824](bd01824)) * **dynamodb:** Avoid tag race condition by using diff-based tag updates ([#6479](#6479)) ([bad2b7d](bad2b7d)), closes [#6418](#6418) * **dynamodb:** Fix mypy type for _build_projection_expression return ([217b4da](217b4da)) * Fix intermittent async test failures for DynamoDB and Redis ([63c5eb1](63c5eb1)) * Fix mongodb blog title ([57d28d4](57d28d4)) * Fix shared SQL registry crash - avoid unnecessary UDF deserialization in proto cache building ([ac588d7](ac588d7)) * Fix SparkRetrievalJob.persist() failing for SparkSource ([209d7cd](209d7cd)) * Fixed formatting and image for mongo blog ([#6377](#6377)) ([f8389fb](f8389fb)) * Fixes for ray source ([7f592a4](7f592a4)) * **go:** skip registry refresh when cache_ttl_seconds <= 0 ([97ed40c](97ed40c)) * Handle array of strings columns in Athena materialization ([#6324](#6324)) ([4ed0278](4ed0278)) * make milvus VARCHAR max_length configurable, remove hardcoded 512 limit ([3b98c22](3b98c22)) * **operator:** Set appProtocol: grpc on registry gRPC Service ([#6367](#6367)) ([c9ae2b4](c9ae2b4)) * PyJWT 2.10+ added validation that rejects empty HMAC keys ([e756ffe](e756ffe)) * RemoteOnlineStore sends all features in a single HTTP request ([8f187dd](8f187dd)) * Remove registry proto dump to enforce RBAC and add permission checks to Commit/Refresh RPCs ([328431f](328431f)) * Remove selector migration job - no longer needed ([51c325e](51c325e)) * replace broken .claude skill symlink with correct relative path ([4541690](4541690)) * Replace selector label strip patch with migration Job for upgrade-safe selector uniqueness ([00dea50](00dea50)) * Scope feature view name conflict check to current project in file-based registry ([#6369](#6369)) ([a4fde83](a4fde83)), closes [#6209](#6209) * **snowflake:** Stop double-quoting connection identifiers ([#6462](#6462)) ([e914d59](e914d59)) * **spark:** S3/GCS PyArrow filesystem resolution for staging paths ([#6442](#6442)) ([ae50414](ae50414)) * **trino:** Clean up temporary entity tables after retrieval ([#6381](#6381)) ([d86b13d](d86b13d)), closes [#6306](#6306) * Update go-feature-server base image to Go 1.25 and fix operator Dockerfile COPY permissions ([86ef0bc](86ef0bc)) ### Features * [Backend] Data Quality Monitoring with native compute, multi-backend support, REST API, CLI ([#6202](#6202)) ([5458c37](5458c37)) * Add apache flink compute engine ([#6476](#6476)) ([9636d6a](9636d6a)) * Add demo noteboooks for users ([e362173](e362173)) * Add enabled/disabled toggle for feature views ([#6401](#6401)) ([5f1fa0d](5f1fa0d)), closes [#6395](#6395) * Add Label View to init template ([ec272d5](ec272d5)) * Add mTLS support to remote registry gRPC client ([#6474](#6474)) ([c9602d8](c9602d8)) * Add Prometheus gauges for FeatureStore installation telemetry ([#6354](#6354)) ([1b681b7](1b681b7)) * Adds registry REST API endpoints for managing entities, data sources, and feature views ([#6413](#6413)) ([f77bd1d](f77bd1d)) * Allow CRUD on entities, data sources, and feature views from UI ([#6412](#6412)) ([2321c07](2321c07)) * Allow default openlineage configuration ([#6467](#6467)) ([276b6df](276b6df)) * **bigquery:** Support DATE-type event timestamp columns ([#6362](#6362)) ([753dee5](753dee5)), closes [#2530](#2530) * **cli:** Add `feast projects delete` command (closes [#5095](#5095)) ([#6318](#6318)) ([1a4b96c](1a4b96c)) * Data Quality Monitoring added in feast UI ([#6422](#6422)) ([fa271be](fa271be)) * **dynamodb:** Use ProjectionExpression when requested_features is set ([0adc906](0adc906)), closes [#6058](#6058) * Enhance DataSource and FeatureView modals with error handling and submission states ([96d7169](96d7169)) * Expose registry endpoints on feature server for MCP access ([f77981c](f77981c)) * Feast First-Class LabelView Implementation ([#6292](#6292)) ([c0e7e5d](c0e7e5d)) * Feast-MLflow Integration ([#6235](#6235)) ([7279c75](7279c75)) * Operational metrics for offline store and SOX metrics for both ([#6340](#6340)) ([65b1b80](65b1b80)) * Pre-compute feature service ([8011550](8011550)) * REST API-backed UI for RBAC compatibility and per-page lazy loading ([#6414](#6414)) ([6ae80af](6ae80af)) * Support non-string map key types ([#6382](#6382)) ([#6383](#6383)) ([728aa2e](728aa2e)) * Update FeatureStore CRD with DRA Fields ([01241e4](01241e4)) ### Performance Improvements * Cache feature view resolution in get_online_features to reduce per-request overhead ([55c2f18](55c2f18)) * Optimize feature serving latency with batched async Redis, cached checks fix ([103809a](103809a)) * Replace MessageToDict with optimized custom dict builder ([#6015](#6015)) ([9902064](9902064))

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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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ntkathole requested changes May 12, 2026

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Comment thread pyproject.toml Outdated

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include mlflow here in ci dependencies

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Vperiodt force-pushed the feast-mlflow branch from fa8fb80 to d798acb Compare May 19, 2026 07:47

Vperiodt added 3 commits May 20, 2026 15:13

fix-CI

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Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED

fix-lint

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Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED

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Merge branch 'master' into feast-mlflow

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fix: remove unused imports in ui_server.py

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Signed-off-by: Vanshika Vanshika <vvanshik@redhat.com> rh-pre-commit.version: 2.3.2 rh-pre-commit.check-secrets: ENABLED

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Merge branch 'master' into feast-mlflow

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ntkathole merged commit 7279c75 into feast-dev:master May 21, 2026
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Vperiodt mentioned this pull request Jun 1, 2026

docs: Add blog post for native MLflow integration #6460

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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:

Key Capabilities

Demo

Which issue(s) this PR fixes:

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Vperiodt commented Apr 8, 2026 •

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