feature: Feat: Feature Store in Sagemaker SDK v3 (5496)

docs/sagemaker_core/index.rst

@@ -151,6 +151,46 @@ Key Core Features
 * **Monitoring Integration** - Built-in support for CloudWatch metrics, logging, and resource status tracking
 * **Error Handling** - Comprehensive error handling with detailed feedback for troubleshooting and debugging
 
+Feature Store
+~~~~~~~~~~~~~
+
+SageMaker Core provides the foundational ``FeatureGroup`` resource class used by the Feature Store module.
+For full Feature Store functionality in V3 — including DataFrame ingestion, Athena queries, dataset building,
+and feature definitions — use the ``sagemaker.mlops.feature_store`` package:
+
+.. code-block:: python
+
+   from sagemaker.mlops.feature_store import (
+       FeatureGroup,
+       OnlineStoreConfig,
+       OfflineStoreConfig,
+       S3StorageConfig,
+       load_feature_definitions_from_dataframe,
+       ingest_dataframe,
+       create_athena_query,
+       DatasetBuilder,
+   )
+
+   # Create a feature group
+   feature_defs = load_feature_definitions_from_dataframe(df)
+   FeatureGroup.create(
+       feature_group_name="my-feature-group",
+       feature_definitions=feature_defs,
+       record_identifier_feature_name="id",
+       event_time_feature_name="timestamp",
+       role_arn=role,
+       online_store_config=OnlineStoreConfig(enable_online_store=True),
+   )
+
+   # Ingest data from a DataFrame
+   ingest_dataframe(feature_group_name="my-feature-group", data_frame=df, max_workers=4)
+
+.. note::
+
+   If you are migrating from V2 (``sagemaker.feature_store``), see the
+   `Feature Store Migration Guide <https://github.com/aws/sagemaker-python-sdk/blob/main/sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md>`_
+   for detailed V2-to-V3 migration instructions.
+
 Supported Core Scenarios
 ------------------------
 

migration.md

@@ -418,6 +418,139 @@ pipeline = Pipeline(
 )
 ```
 
+### 5. Feature Store
+
+Feature Store is fully supported in V3 under the `sagemaker.mlops.feature_store` namespace. The V2 `sagemaker.feature_store` module has been reorganized — FeatureGroup and shapes come from `sagemaker-core`, while utility functions, ingestion, Athena queries, and dataset building are provided by the `sagemaker-mlops` package.
+
+> **Detailed Migration Guide:** For comprehensive V2-to-V3 Feature Store migration instructions, see [`sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md`](sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md).
+
+**Import Changes:**
+
+```python
+# V2 imports
+from sagemaker.feature_store.feature_group import FeatureGroup  # ❌
+from sagemaker.feature_store.inputs import FeatureValue          # ❌
+from sagemaker.session import Session                            # ❌
+
+# V3 imports — everything from one place
+from sagemaker.mlops.feature_store import FeatureGroup           # ✅
+from sagemaker.mlops.feature_store import FeatureValue           # ✅
+from sagemaker.mlops.feature_store import ingest_dataframe       # ✅
+```
+
+**Create a FeatureGroup:**
+
+**V2:**
+
+```python
+from sagemaker.feature_store.feature_group import FeatureGroup
+from sagemaker.session import Session
+
+session = Session()
+fg = FeatureGroup(name="my-fg", sagemaker_session=session)
+fg.load_feature_definitions(data_frame=df)
+fg.create(
+    s3_uri="s3://bucket/prefix",
+    record_identifier_name="id",
+    event_time_feature_name="ts",
+    role_arn=role,
+    enable_online_store=True,
+)
+```
+
+**V3:**
+
+```python
+from sagemaker.mlops.feature_store import (
+    FeatureGroup,
+    OnlineStoreConfig,
+    OfflineStoreConfig,
+    S3StorageConfig,
+    load_feature_definitions_from_dataframe,
+)
+
+feature_defs = load_feature_definitions_from_dataframe(df)
+
+FeatureGroup.create(
+    feature_group_name="my-fg",
+    feature_definitions=feature_defs,
+    record_identifier_feature_name="id",
+    event_time_feature_name="ts",
+    role_arn=role,
+    online_store_config=OnlineStoreConfig(enable_online_store=True),
+    offline_store_config=OfflineStoreConfig(
+        s3_storage_config=S3StorageConfig(s3_uri="s3://bucket/prefix")
+    ),
+)
+```
+
+**Record Operations (Put/Get/Delete):**
+
+**V2:**
+
+```python
+from sagemaker.feature_store.inputs import FeatureValue
+
+fg.put_record(record=[FeatureValue(feature_name="id", value_as_string="123")])
+response = fg.get_record(record_identifier_value_as_string="123")
+fg.delete_record(record_identifier_value_as_string="123", event_time="2024-01-15T00:00:00Z")
+```
+
+**V3:**
+
+```python
+from sagemaker.mlops.feature_store import FeatureGroup, FeatureValue
+
+fg = FeatureGroup(feature_group_name="my-fg")
+fg.put_record(record=[FeatureValue(feature_name="id", value_as_string="123")])
+response = fg.get_record(record_identifier_value_as_string="123")
+fg.delete_record(record_identifier_value_as_string="123", event_time="2024-01-15T00:00:00Z")
+```
+
+**DataFrame Ingestion:**
+
+**V2:**
+
+```python
+fg.ingest(data_frame=df, max_workers=4, max_processes=2, wait=True)
+```
+
+**V3:**
+
+```python
+from sagemaker.mlops.feature_store import ingest_dataframe
+
+manager = ingest_dataframe(
+    feature_group_name="my-fg",
+    data_frame=df,
+    max_workers=4,
+    max_processes=2,
+    wait=True,
+)
+```
+
+**Athena Queries:**
+
+**V2:**
+
+```python
+query = fg.athena_query()
+query.run(query_string="SELECT * FROM ...", output_location="s3://...")
+query.wait()
+df = query.as_dataframe()
+```
+
+**V3:**
+
+```python
+from sagemaker.mlops.feature_store import create_athena_query
+
+query = create_athena_query("my-fg", session)
+query.run(query_string="SELECT * FROM ...", output_location="s3://...")
+query.wait()
+df = query.as_dataframe()
+```
+
 ## Feature Mapping
 
 ### Training Features
@@ -450,6 +583,27 @@ pipeline = Pipeline(
 | ScriptProcessor | ProcessingJob | Script-based processing |
 | FrameworkProcessor | ProcessingJob | Framework-specific processing |
 
+### Feature Store Features
+
+| V2 Feature | V3 Equivalent | Notes |
+|------------|---------------|-------|
+| `sagemaker.feature_store.feature_group.FeatureGroup` | `sagemaker.mlops.feature_store.FeatureGroup` | Re-exported from sagemaker-core |
+| `FeatureGroup(name=..., sagemaker_session=...)` | `FeatureGroup(feature_group_name=...)` | Session managed internally by core |
+| `fg.create(s3_uri=..., enable_online_store=...)` | `FeatureGroup.create(online_store_config=..., offline_store_config=...)` | Structured config objects |
+| `fg.describe()` | `FeatureGroup.get(feature_group_name=...)` | Returns typed object |
+| `fg.delete()` | `FeatureGroup(feature_group_name=...).delete()` | Same pattern |
+| `fg.put_record(record=...)` | `FeatureGroup(feature_group_name=...).put_record(record=...)` | FeatureValue from core |
+| `fg.get_record(...)` | `FeatureGroup(feature_group_name=...).get_record(...)` | Same interface |
+| `fg.delete_record(...)` | `FeatureGroup(feature_group_name=...).delete_record(...)` | Use strings not enums |
+| `fg.ingest(data_frame=df)` | `ingest_dataframe(feature_group_name=..., data_frame=df)` | Standalone function |
+| `fg.athena_query()` | `create_athena_query(feature_group_name, session)` | Standalone function |
+| `fg.as_hive_ddl()` | `as_hive_ddl(feature_group_name)` | Standalone function |
+| `fg.load_feature_definitions(df)` | `load_feature_definitions_from_dataframe(df)` | Returns list, no mutation |
+| `FeatureStore(session).search(...)` | `FeatureStore.search(resource=..., search_expression=...)` | Core resource class |
+| `FeatureStore.create_dataset(...)` | `DatasetBuilder.create(...)` | Dataclass-based builder |
+| Config shapes with `to_dict()` | Pydantic shapes (auto-serialization) | No manual serialization needed |
+| `TargetStoreEnum.ONLINE_STORE.value` | `"OnlineStore"` (plain strings) | Enums available but strings preferred |
+
 ## Functionality Level Mapping
 
 ### Training
@@ -766,6 +920,12 @@ from sagemaker.train import ModelTrainer   # ✅
 
 from sagemaker.model import Model          # ❌
 from sagemaker.serve import ModelBuilder   # ✅
+
+from sagemaker.feature_store.feature_group import FeatureGroup  # ❌
+from sagemaker.mlops.feature_store import FeatureGroup          # ✅
+
+from sagemaker.feature_store.inputs import FeatureValue         # ❌
+from sagemaker.mlops.feature_store import FeatureValue          # ✅
 ```
 
 ### 2. Parameter Mapping

sagemaker-mlops/README.md

@@ -77,6 +77,51 @@ The following files were moved from `sagemaker-core/src/sagemaker/core/workflow/
 - `retry.py` - Retry policies
 - `selective_execution_config.py` - Selective execution settings
 
+### Feature Store
+
+The Feature Store module (`sagemaker.mlops.feature_store`) provides comprehensive support for Amazon SageMaker Feature Store operations. This is the V3 equivalent of the V2 `sagemaker.feature_store` module.
+
+**Key Modules:**
+
+- `__init__.py` - Re-exports all Feature Store components from a single entry point
+- `feature_definition.py` - Feature definition helpers (FractionalFeatureDefinition, IntegralFeatureDefinition, etc.)
+- `feature_utils.py` - Utility functions (ingest_dataframe, create_athena_query, as_hive_ddl, etc.)
+- `ingestion_manager_pandas.py` - Multi-threaded DataFrame ingestion manager
+- `athena_query.py` - Athena query execution and result retrieval
+- `dataset_builder.py` - Dataset building with point-in-time joins across feature groups
+- `inputs.py` - Enums for Feature Store operations (TargetStoreEnum, DeletionModeEnum, etc.)
+- `feature_processor/` - Feature processor for PySpark-based transformations
+
+**Quick Start:**
+
+```python
+from sagemaker.mlops.feature_store import (
+    FeatureGroup,
+    OnlineStoreConfig,
+    OfflineStoreConfig,
+    S3StorageConfig,
+    load_feature_definitions_from_dataframe,
+    ingest_dataframe,
+    create_athena_query,
+)
+
+# Create a feature group
+feature_defs = load_feature_definitions_from_dataframe(df)
+FeatureGroup.create(
+    feature_group_name="my-feature-group",
+    feature_definitions=feature_defs,
+    record_identifier_feature_name="id",
+    event_time_feature_name="timestamp",
+    role_arn=role,
+    online_store_config=OnlineStoreConfig(enable_online_store=True),
+)
+
+# Ingest data
+ingest_dataframe(feature_group_name="my-feature-group", data_frame=df, max_workers=4)
+```
+
+> **Migrating from V2?** See the detailed [Feature Store Migration Guide](src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md) for V2-to-V3 migration instructions.
+
 ### Model Building
 
 ModelBuilder is now located in the `sagemaker-serve` package but is re-exported from MLOps for convenience.

sagemaker-mlops/src/sagemaker/mlops/__init__.py

@@ -1,7 +1,8 @@
 """SageMaker MLOps package for workflow orchestration and model building.
 
 This package provides high-level orchestration capabilities for SageMaker workflows,
-including pipeline definitions, step implementations, and model building utilities.
+including pipeline definitions, step implementations, model building utilities,
+and Feature Store operations.
 
 The MLOps package sits at the top of the dependency hierarchy and can import from:
 - sagemaker.core (foundation primitives)
@@ -11,10 +12,12 @@
 Key components:
 - workflow: Pipeline and step orchestration
 - model_builder: Model building and orchestration
+- feature_store: Feature Store operations (FeatureGroup, ingestion, Athena queries)
 
 Example usage:
     from sagemaker.mlops import ModelBuilder
     from sagemaker.mlops.workflow import Pipeline, TrainingStep
+    from sagemaker.mlops.feature_store import FeatureGroup, ingest_dataframe
 """
 from __future__ import absolute_import
 
@@ -27,7 +30,12 @@
 #   from sagemaker.mlops import workflow
 #   from sagemaker.mlops.workflow import Pipeline, TrainingStep, etc.
 
+# Feature Store submodule is available via:
+#   from sagemaker.mlops import feature_store
+#   from sagemaker.mlops.feature_store import FeatureGroup, ingest_dataframe, etc.
+
 __all__ = [
     "ModelBuilder",
     "workflow",  # Submodule
+    "feature_store",  # Submodule - Feature Store operations
 ]