feast-dev
diff --git a/.secrets.baseline b/.secrets.baseline
@@ -1539,5 +1539,5 @@
       }
     ]
   },
-  "generated_at": "2026-05-14T10:20:01Z"
+  "generated_at": "2026-05-20T07:55:48Z"
 }
@@ -0,0 +1,347 @@
+# MLflow Integration
+
+Feast provides **native integration** with [MLflow](https://mlflow.org/) for automatic feature lineage tracking alongside ML experiments. When enabled, every feature retrieval is logged to the active MLflow run.
+
+## Overview
+
+- **Which features did this model use?** -- auto-logged on every `get_historical_features()` / `get_online_features()` call
+- **Which feature service should I use to serve this model?** -- resolved from model URI via `store.mlflow.resolve_features()`
+- **Can I reproduce the exact training data?** -- entity DataFrame saved as an MLflow artifact
+- **Which models break if I change a feature view?** -- reverse index via the Feast UI `/api/mlflow-feature-usage` endpoint
+- **When was the feature store last updated?** -- `feast apply` and `feast materialize` logged to a separate ops experiment
+
+### Capabilities
+
+| Capability | How |
+|---|---|
+| Auto-log feature metadata | Tags on every retrieval inside an active MLflow run |
+| Entity DataFrame archival | `entity_df.parquet` artifact for full reproducibility |
+| Model registration with lineage | `feast.feature_service` tag propagated to model versions |
+| Training-to-prediction linkage | `store.mlflow.load_model()` links prediction runs back to training runs |
+| Model-to-feature resolution | Map any model URI back to its Feast feature service |
+| Operation audit trail | `feast apply` / `feast materialize` logged to `{project}-feast-ops` |
+| `store.mlflow` API | Single entry point — zero `import mlflow`, zero client objects |
+| Feast UI integration | Per-feature-view usage stats and registered model associations |
+
+## Installation
+
+MLflow is an optional dependency:
+
+```bash
+pip install feast[mlflow]
+```
+
+## Configuration
+
+Add the `mlflow` section to your `feature_store.yaml`:
+
+```yaml
+project: my_project
+registry: data/registry.db
+provider: local
+online_store:
+  type: sqlite
+  path: data/online_store.db
+
+mlflow:
+  enabled: true
+  tracking_uri: http://127.0.0.1:5000   # optional, falls back to MLFLOW_TRACKING_URI env var
+  auto_log: true                         # default
+  auto_log_entity_df: false              # default
+  entity_df_max_rows: 100000             # default
+  log_operations: false                  # default
+  ops_experiment_suffix: "-feast-ops"    # default
+```
+
+### Configuration options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `enabled` | bool | `false` | Master switch for the entire integration |
+| `tracking_uri` | string | *(none)* | MLflow tracking server URI. Falls back to `MLFLOW_TRACKING_URI` env var, then MLflow default (`./mlruns`) |
+| `auto_log` | bool | `true` | Automatically log feature metadata on every retrieval when an active MLflow run exists |
+| `auto_log_entity_df` | bool | `false` | Save the entity DataFrame as `entity_df.parquet` artifact on historical retrieval |
+| `entity_df_max_rows` | int | `100000` | Skip entity DataFrame artifact upload for DataFrames exceeding this limit |
+| `log_operations` | bool | `false` | Log `feast apply` and `feast materialize` to a separate MLflow experiment |
+| `ops_experiment_suffix` | string | `"-feast-ops"` | Suffix appended to project name for the operations experiment |
+
+### Tracking URI resolution
+
+The tracking URI is resolved in this order:
+
+1. `tracking_uri` field in `feature_store.yaml`
+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, or it would be pulled from `./mlruns` automatically when both are not set.
+
+## What gets logged
+
+### Tags on retrieval runs
+
+When `auto_log: true` and an active MLflow run exists, each `get_historical_features()` or `get_online_features()` call records:
+
+| Tag | Example | Description |
+|-----|---------|-------------|
+| `feast.project` | `my_project` | Feast project name |
+| `feast.retrieval_type` | `historical` / `online` | Type of feature retrieval |
+| `feast.feature_service` | `driver_activity_v1` | Auto-resolved feature service name (if matched) |
+| `feast.feature_views` | `driver_hourly_stats` | Comma-separated feature view names |
+| `feast.feature_refs` | `driver_hourly_stats:conv_rate,...` | All feature references |
+| `feast.entity_count` | `200` | Number of entities in the request |
+| `feast.feature_count` | `5` | Number of features retrieved |
+
+### Metrics
+
+| Metric | Example | Description |
+|--------|---------|-------------|
+| `feast.job_submission_sec` | `0.4321` | Feature retrieval duration in seconds |
+
+### Artifacts
+
+When `auto_log_entity_df: true` and the entity DataFrame has fewer than `entity_df_max_rows` rows:
+
+| Artifact | Description |
+|----------|-------------|
+| `entity_df.parquet` | Full entity DataFrame used in the retrieval |
+
+When a model is logged via `store.mlflow.log_model()`:
+
+| Artifact | Description |
+|----------|-------------|
+| `feast_features.json` | JSON list of feature references the model was trained on |
+
+### Entity DataFrame metadata
+
+Regardless of `auto_log_entity_df`, the following metadata is logged when present:
+
+| Tag / Param | When | Description |
+|-------------|------|-------------|
+| `feast.entity_df_type` | Always | `dataframe`, `sql`, or `range` |
+| `feast.entity_df_rows` | DataFrame input | Row count |
+| `feast.entity_df_columns` | DataFrame input | Column names |
+| `feast.entity_df_query` | SQL input | The SQL query string |
+| `feast.start_date` / `feast.end_date` | Range-based input | Date range |
+
+### Operation logs
+
+When `log_operations: true`, `feast apply` and `feast materialize` create self-contained runs in the `{project}{ops_experiment_suffix}` experiment (default: `my_project-feast-ops`):
+
+**Apply runs:**
+
+| Tag / Metric | Example |
+|--------------|---------|
+| `feast.operation` | `apply` |
+| `feast.project` | `my_project` |
+| `feast.feature_views_changed` | `driver_hourly_stats,order_stats` |
+| `feast.feature_services_changed` | `driver_activity_v1` |
+| `feast.entities_changed` | `driver,restaurant` |
+| `feast.apply.feature_views_count` | `2` |
+| `feast.apply.feature_services_count` | `1` |
+| `feast.apply.entities_count` | `2` |
+
+**Materialize runs:**
+
+| Tag / Metric | Example |
+|--------------|---------|
+| `feast.operation` | `materialize` / `materialize_incremental` |
+| `feast.project` | `my_project` |
+| `feast.materialize.feature_views` | `driver_hourly_stats` |
+| `feast.materialize.start_date` | `2024-01-01T00:00:00` |
+| `feast.materialize.end_date` | `2024-01-02T00:00:00` |
+| `feast.materialize.duration_sec` | `12.3456` |
+
+## Usage
+
+### Automatic logging (zero code)
+
+With the configuration above, feature metadata is logged automatically whenever there is an active MLflow run. No explicit `import mlflow` is needed — just use `store.mlflow`:
+
+```python
+from feast import FeatureStore
+
+store = FeatureStore(".")
+
+with store.mlflow.start_run(run_name="my_training"):
+    training_df = store.get_historical_features(
+        features=store.get_feature_service("driver_activity_v1"),
+        entity_df=entity_df,
+    ).to_df()
+    # The run is now tagged with feast.feature_refs, feast.feature_views, etc.
+
+    model = train(training_df)
+    store.mlflow.log_model(model, "model")
+```
+
+No extra code needed — the tags are written automatically.
+
+### `store.mlflow` API (recommended)
+
+`store.mlflow` is the primary way to interact with the Feast–MLflow integration. It provides Feast-enhanced versions of common MLflow operations, and delegates everything else to the raw `mlflow` module:
+
+```python
+from feast import FeatureStore
+from sklearn.linear_model import LogisticRegression
+
+store = FeatureStore(".")
+
+# Training
+with store.mlflow.start_run(run_name="v1_training"):
+    df = store.get_historical_features(
+        features=store.get_feature_service("driver_activity_v1"),
+        entity_df=entity_df,
+    ).to_df()
+
+    model = LogisticRegression().fit(X, y)
+    store.mlflow.log_model(model, "model")     # Feast-enhanced: saves feast_features.json
+    train_run_id = store.mlflow.active_run_id
+
+# Register model (auto-tags version with feast.feature_service)
+store.mlflow.register_model(f"runs:/{train_run_id}/model", "driver_model")
+
+# Prediction (auto-links to training run)
+with store.mlflow.start_run(run_name="prediction"):
+    model = store.mlflow.load_model("models:/driver_model/1")
+    online_features = store.get_online_features(
+        features=store.get_feature_service("driver_activity_v1"),
+        entity_rows=[{"driver_id": 1001}],
+    )
+    predictions = model.predict(...)
+```
+
+### `feast.mlflow` module API (alternative)
+
+For users who prefer a module-level import, `feast.mlflow` is a **drop-in replacement for `import mlflow`** that delegates to the same `store.mlflow` client under the hood:
+
+```python
+import feast.mlflow
+from feast import FeatureStore
+
+store = FeatureStore(".")   # auto-registers with feast.mlflow
+
+with feast.mlflow.start_run(run_name="training"):
+    df = store.get_historical_features(...).to_df()
+    feast.mlflow.log_params({"lr": "0.01"})     # plain passthrough
+    feast.mlflow.log_metrics({"f1": 0.85})       # plain passthrough
+    feast.mlflow.log_model(model, "model")       # Feast-enhanced
+```
+
+#### Store resolution
+
+`feast.mlflow` resolves its `FeatureStore` in this order:
+
+1. **Explicit `feast.mlflow.init(store)`** — if called, overrides everything
+2. **Auto-registered** — the most recently created `FeatureStore` with `mlflow.enabled=true` registers itself automatically
+3. **Auto-discovery** — falls back to `FeatureStore(".")` from the current directory
+
+In most cases, simply creating a `FeatureStore(...)` is enough — no `init()` needed.
+
+#### Error handling
+
+`feast.mlflow` raises clear errors on first use if something is misconfigured:
+
+| Condition | Error |
+|-----------|-------|
+| No `feature_store.yaml` in cwd and no store created | `RuntimeError` with guidance to call `feast.mlflow.init(store)` |
+| `mlflow.enabled` is not set to `true` | `RuntimeError` with guidance to set `mlflow.enabled=true` |
+| `mlflow` pip package not installed | `ImportError` with guidance to run `pip install feast[mlflow]` |
+
+When `mlflow.enabled` is `false` (or omitted), `store.mlflow` returns `None`, allowing callers to guard with `if store.mlflow:`. The `feast.mlflow` module raises `RuntimeError` only when you attempt to use it without an enabled store.
+
+### Feast-enhanced functions
+
+These functions add automatic Feast tagging and lineage on top of their MLflow counterparts:
+
+| Function | Enhancement |
+|----------|-------------|
+| `store.mlflow.start_run(run_name, tags)` | Auto-tags run with `feast.project` |
+| `store.mlflow.log_model(model, path, flavor)` | Auto-attaches `feast_features.json` artifact |
+| `store.mlflow.register_model(model_uri, name)` | Auto-tags model version with `feast.feature_service` |
+| `store.mlflow.load_model(model_uri)` | Auto-tags prediction run with training lineage |
+
+**Supported model flavors for `log_model()`:** `sklearn`, `pytorch`, `xgboost`, `lightgbm`, `tensorflow`, `keras`, `pyfunc`.
+
+### Feast-only functions
+
+These are unique to the Feast integration and have no `mlflow` equivalent:
+
+| Function | Description |
+|----------|-------------|
+| `store.mlflow.resolve_features(model_uri)` | Resolve model URI to Feast feature service name |
+| `store.mlflow.get_training_entity_df(run_id, ...)` | Recover entity DataFrame from a past MLflow run |
+| `store.mlflow.log_training_dataset(df, dataset_name)` | Log a training DataFrame as an MLflow dataset input |
+| `store.mlflow.active_run_id` | Current active MLflow run ID (or `None`) |
+| `store.mlflow.client` | The underlying `MlflowClient` instance for advanced queries |
+| `feast.mlflow.init(store)` | Explicitly bind `feast.mlflow` module to a `FeatureStore` (optional) |
+
+### Passthrough behavior
+
+The `feast.mlflow` module delegates any attribute not listed above to the raw `mlflow` module. This means you can use `feast.mlflow` as a drop-in replacement for `import mlflow`:
+
+```python
+feast.mlflow.log_params(params)             # passes through to mlflow.log_params
+feast.mlflow.log_metrics(metrics)
+feast.mlflow.set_tag("env", "staging")
+feast.mlflow.MlflowClient()
+```
+
+`store.mlflow` does **not** have this passthrough — it only exposes the Feast-enhanced and Feast-only methods listed above. To access raw `mlflow` functions from `store.mlflow`, use the escape hatches:
+
+```python
+store.mlflow.client.log_param(run_id, "lr", "0.01")  # via MlflowClient instance
+store.mlflow.mlflow.log_params(params)                # via raw mlflow module
+```
+
+### Resolve a model back to its feature service
+
+```python
+from feast import FeatureStore
+
+store = FeatureStore(".")
+fs_name = store.mlflow.resolve_features("models:/driver_model/1")
+# Returns: "driver_activity_v1"
+```
+
+Resolution order:
+1. Model version tag `feast.feature_service` (set by `register_model()`)
+2. Training run tag `feast.feature_service` (set by auto-logging)
+
+### Reproduce training from a past run
+
+```python
+from feast import FeatureStore
+
+store = FeatureStore(".")
+
+entity_df = store.mlflow.get_training_entity_df(run_id="abc123")
+
+with store.mlflow.start_run(run_name="retrain_v2"):
+    new_df = store.get_historical_features(
+        features=store.get_feature_service("driver_activity_v1"),
+        entity_df=entity_df,
+    ).to_df()
+    model = train(new_df)
+    store.mlflow.log_model(model, "model")
+```
+
+This requires `auto_log_entity_df: true` to have been enabled when the original run was recorded.
+
+## Feast UI integration
+
+The Feast UI server exposes three API endpoints that aggregate data from MLflow:
+
+| Endpoint | Description |
+|----------|-------------|
+| `/api/mlflow-runs` | All Feast-tagged MLflow runs with linked registered models |
+| `/api/mlflow-feature-usage` | Per-feature-view usage stats (run count, last used, associated models) |
+| `/api/mlflow-feature-models` | Reverse index of feature refs to registered models |
+
+The feature view detail page in the Feast UI displays:
+- **MLflow Training Runs** count and **Last Used** date in the header stats
+- An **MLflow Usage** panel showing training run count, relative last-used time, and a table of registered models that depend on the feature view
+
+Start the Feast UI with:
+
+```bash
+feast ui --host 127.0.0.1 --port 8888
+```
-Original file line number
+Diff line change
@@ Expand Up / @@ -1539,5 +1539,5 @@ @@
           }
         ]
       },
-      "generated_at": "2026-05-14T10:20:01Z"
+      "generated_at": "2026-05-20T07:55:48Z"
     }