diff --git a/sagemaker-mlops/src/sagemaker/mlops/__init__.py b/sagemaker-mlops/src/sagemaker/mlops/__init__.py
index 18527db318..0abf7a8c36 100644
--- a/sagemaker-mlops/src/sagemaker/mlops/__init__.py
+++ b/sagemaker-mlops/src/sagemaker/mlops/__init__.py
@@ -11,10 +11,21 @@
 Key components:
 - workflow: Pipeline and step orchestration
 - model_builder: Model building and orchestration
+- feature_store: Feature Store management (FeatureGroup, FeatureStore, ingestion, etc.)
 
 Example usage:
     from sagemaker.mlops import ModelBuilder
     from sagemaker.mlops.workflow import Pipeline, TrainingStep
+
+    # Feature Store
+    from sagemaker.mlops.feature_store import (
+        FeatureGroup,
+        FeatureStore,
+        FeatureMetadata,
+        create_dataset,
+        ingest_dataframe,
+        create_athena_query,
+    )
 """
 from __future__ import absolute_import
 
@@ -27,7 +38,12 @@
 #   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, FeatureStore, create_dataset, etc.
+
 __all__ = [
     "ModelBuilder",
     "workflow",  # Submodule
+    "feature_store",  # Submodule
 ]
diff --git a/sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md b/sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md
index 40942fa6f3..2ea2d81796 100644
--- a/sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md
+++ b/sagemaker-mlops/src/sagemaker/mlops/feature_store/MIGRATION_GUIDE.md
@@ -471,7 +471,7 @@ These V2 features are **not wrapped** because core provides them directly:
 from sagemaker.mlops.feature_store import (
     # Resources (from core)
     FeatureGroup,
-    FeatureStore,
+    FeatureStore,  # For search() and batch operations
     FeatureMetadata,
     
     # Shapes (from core)
diff --git a/sagemaker-mlops/src/sagemaker/mlops/feature_store/__init__.py b/sagemaker-mlops/src/sagemaker/mlops/feature_store/__init__.py
index f15d6d3845..d4244d5805 100644
--- a/sagemaker-mlops/src/sagemaker/mlops/feature_store/__init__.py
+++ b/sagemaker-mlops/src/sagemaker/mlops/feature_store/__init__.py
@@ -3,7 +3,7 @@
 """SageMaker FeatureStore V3 - powered by sagemaker-core."""
 
 # Resources from core
-from sagemaker.core.resources import FeatureGroup, FeatureMetadata
+from sagemaker.core.resources import FeatureGroup, FeatureMetadata, FeatureStore
 
 # Shapes from core (Pydantic - no to_dict() needed)
 from sagemaker.core.shapes import (
@@ -51,6 +51,7 @@
 from sagemaker.mlops.feature_store.feature_utils import (
     as_hive_ddl,
     create_athena_query,
+    create_dataset,
     get_session_from_role,
     ingest_dataframe,
     load_feature_definitions_from_dataframe,
@@ -74,6 +75,7 @@
     # Resources
     "FeatureGroup",
     "FeatureMetadata",
+    "FeatureStore",
     # Shapes
     "DataCatalogConfig",
     "FeatureParameter",
@@ -110,6 +112,7 @@
     # Utility functions
     "as_hive_ddl",
     "create_athena_query",
+    "create_dataset",
     "get_session_from_role",
     "ingest_dataframe",
     "load_feature_definitions_from_dataframe",
diff --git a/sagemaker-mlops/src/sagemaker/mlops/feature_store/feature_utils.py b/sagemaker-mlops/src/sagemaker/mlops/feature_store/feature_utils.py
index 5ee04780be..6e00d53d6b 100644
--- a/sagemaker-mlops/src/sagemaker/mlops/feature_store/feature_utils.py
+++ b/sagemaker-mlops/src/sagemaker/mlops/feature_store/feature_utils.py
@@ -421,6 +421,64 @@ def as_hive_ddl(
     return ddl
 
 
+@_telemetry_emitter(Feature.FEATURE_STORE, "create_dataset")
+def create_dataset(
+    base,
+    output_path: str,
+    session: Session,
+    record_identifier_feature_name: str = None,
+    event_time_identifier_feature_name: str = None,
+    included_feature_names=None,
+    kms_key_id: str = None,
+):
+    """Create a DatasetBuilder for generating a Dataset from FeatureGroups.
+
+    This is a convenience function that constructs a DatasetBuilder instance.
+    The base can be either a FeatureGroup or a pandas DataFrame.
+
+    Args:
+        base (Union[FeatureGroup, DataFrame]): A FeatureGroup or DataFrame to use as the base.
+        output_path (str): S3 URI for storing query results.
+        session (Session): SageMaker session for boto calls.
+        record_identifier_feature_name (str): Required if base is a DataFrame.
+            The feature name used as the record identifier (default: None).
+        event_time_identifier_feature_name (str): Required if base is a DataFrame.
+            The feature name used as the event time identifier (default: None).
+        included_feature_names (List[str]): Features to include in the output.
+            If not set, all features will be included (default: None).
+        kms_key_id (str): KMS key for encryption (default: None).
+
+    Returns:
+        DatasetBuilder: A DatasetBuilder instance configured with the provided parameters.
+
+    Raises:
+        ValueError: If base is a DataFrame and record_identifier_feature_name or
+            event_time_identifier_feature_name is not provided.
+
+    Example:
+        >>> from sagemaker.mlops.feature_store import create_dataset, FeatureGroup
+        >>> fg = FeatureGroup.get(feature_group_name="my-fg")
+        >>> builder = create_dataset(
+        ...     base=fg,
+        ...     output_path="s3://bucket/output",
+        ...     session=session,
+        ... )
+        >>> builder.with_feature_group(other_fg, target_feature_name_in_base="id")
+        >>> df, query = builder.to_dataframe()
+    """
+    from sagemaker.mlops.feature_store.dataset_builder import DatasetBuilder
+
+    return DatasetBuilder.create(
+        base=base,
+        output_path=output_path,
+        session=session,
+        record_identifier_feature_name=record_identifier_feature_name,
+        event_time_identifier_feature_name=event_time_identifier_feature_name,
+        included_feature_names=included_feature_names,
+        kms_key_id=kms_key_id,
+    )
+
+
 @_telemetry_emitter(Feature.FEATURE_STORE, "ingest_dataframe")
 def ingest_dataframe(
     feature_group_name: str,
diff --git a/sagemaker-mlops/tests/unit/sagemaker/mlops/feature_store/test_feature_store_init.py b/sagemaker-mlops/tests/unit/sagemaker/mlops/feature_store/test_feature_store_init.py
new file mode 100644
index 0000000000..cd459a8b63
--- /dev/null
+++ b/sagemaker-mlops/tests/unit/sagemaker/mlops/feature_store/test_feature_store_init.py
@@ -0,0 +1,276 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0
+"""Unit tests for feature_store __init__.py imports and create_dataset function."""
+import pytest
+from unittest.mock import Mock, MagicMock, patch
+import pandas as pd
+
+
+class TestFeatureStoreImports:
+    """Test that all expected symbols are importable from sagemaker.mlops.feature_store."""
+
+    def test_feature_group_importable(self):
+        from sagemaker.mlops.feature_store import FeatureGroup
+        assert FeatureGroup is not None
+
+    def test_feature_store_importable(self):
+        from sagemaker.mlops.feature_store import FeatureStore
+        assert FeatureStore is not None
+
+    def test_feature_metadata_importable(self):
+        from sagemaker.mlops.feature_store import FeatureMetadata
+        assert FeatureMetadata is not None
+
+    def test_create_dataset_importable(self):
+        from sagemaker.mlops.feature_store import create_dataset
+        assert create_dataset is not None
+        assert callable(create_dataset)
+
+    def test_all_shapes_importable(self):
+        from sagemaker.mlops.feature_store import (
+            OnlineStoreConfig,
+            OfflineStoreConfig,
+            S3StorageConfig,
+            TtlDuration,
+            FeatureValue,
+            FeatureParameter,
+            ThroughputConfig,
+            Filter,
+            SearchExpression,
+            DataCatalogConfig,
+            OnlineStoreSecurityConfig,
+        )
+        assert OnlineStoreConfig is not None
+        assert OfflineStoreConfig is not None
+        assert S3StorageConfig is not None
+        assert TtlDuration is not None
+        assert FeatureValue is not None
+        assert FeatureParameter is not None
+        assert ThroughputConfig is not None
+        assert Filter is not None
+        assert SearchExpression is not None
+        assert DataCatalogConfig is not None
+        assert OnlineStoreSecurityConfig is not None
+
+    def test_all_enums_importable(self):
+        from sagemaker.mlops.feature_store import (
+            TargetStoreEnum,
+            DeletionModeEnum,
+            OnlineStoreStorageTypeEnum,
+            TableFormatEnum,
+            ResourceEnum,
+            SearchOperatorEnum,
+            SortOrderEnum,
+            FilterOperatorEnum,
+            ExpirationTimeResponseEnum,
+            ThroughputModeEnum,
+        )
+        assert TargetStoreEnum is not None
+        assert DeletionModeEnum is not None
+        assert OnlineStoreStorageTypeEnum is not None
+        assert TableFormatEnum is not None
+        assert ResourceEnum is not None
+        assert SearchOperatorEnum is not None
+        assert SortOrderEnum is not None
+        assert FilterOperatorEnum is not None
+        assert ExpirationTimeResponseEnum is not None
+        assert ThroughputModeEnum is not None
+
+    def test_all_feature_definition_helpers_importable(self):
+        from sagemaker.mlops.feature_store import (
+            FeatureDefinition,
+            FractionalFeatureDefinition,
+            IntegralFeatureDefinition,
+            StringFeatureDefinition,
+            VectorCollectionType,
+            ListCollectionType,
+            SetCollectionType,
+            FeatureTypeEnum,
+            CollectionTypeEnum,
+        )
+        assert FeatureDefinition is not None
+        assert FractionalFeatureDefinition is not None
+        assert IntegralFeatureDefinition is not None
+        assert StringFeatureDefinition is not None
+        assert VectorCollectionType is not None
+        assert ListCollectionType is not None
+        assert SetCollectionType is not None
+        assert FeatureTypeEnum is not None
+        assert CollectionTypeEnum is not None
+
+    def test_all_utility_functions_importable(self):
+        from sagemaker.mlops.feature_store import (
+            create_athena_query,
+            as_hive_ddl,
+            ingest_dataframe,
+            load_feature_definitions_from_dataframe,
+            create_dataset,
+            get_session_from_role,
+        )
+        assert callable(create_athena_query)
+        assert callable(as_hive_ddl)
+        assert callable(ingest_dataframe)
+        assert callable(load_feature_definitions_from_dataframe)
+        assert callable(create_dataset)
+        assert callable(get_session_from_role)
+
+    def test_all_classes_importable(self):
+        from sagemaker.mlops.feature_store import (
+            AthenaQuery,
+            DatasetBuilder,
+            FeatureGroupToBeMerged,
+            IngestionError,
+            IngestionManagerPandas,
+            JoinComparatorEnum,
+            JoinTypeEnum,
+            TableType,
+        )
+        assert AthenaQuery is not None
+        assert DatasetBuilder is not None
+        assert FeatureGroupToBeMerged is not None
+        assert IngestionError is not None
+        assert IngestionManagerPandas is not None
+        assert JoinComparatorEnum is not None
+        assert JoinTypeEnum is not None
+        assert TableType is not None
+
+    def test_feature_store_in_all(self):
+        import sagemaker.mlops.feature_store as fs_module
+        assert "FeatureStore" in fs_module.__all__
+        assert "FeatureGroup" in fs_module.__all__
+        assert "FeatureMetadata" in fs_module.__all__
+        assert "create_dataset" in fs_module.__all__
+
+
+class TestMlopsInitExposesFeatureStore:
+    """Test that feature_store is accessible via sagemaker.mlops."""
+
+    def test_feature_store_submodule_accessible(self):
+        import sagemaker.mlops
+        assert hasattr(sagemaker.mlops, 'feature_store') or 'feature_store' in sagemaker.mlops.__all__
+
+    def test_feature_store_in_mlops_all(self):
+        import sagemaker.mlops
+        assert "feature_store" in sagemaker.mlops.__all__
+
+    def test_import_from_mlops_feature_store(self):
+        """Verify the full import path works."""
+        from sagemaker.mlops.feature_store import FeatureGroup, FeatureStore, create_dataset
+        assert FeatureGroup is not None
+        assert FeatureStore is not None
+        assert create_dataset is not None
+
+
+class TestCreateDatasetFunction:
+    """Test the create_dataset convenience function."""
+
+    @patch("sagemaker.mlops.feature_store.feature_utils.DatasetBuilder")
+    def test_create_dataset_with_feature_group_base(self, mock_builder_class):
+        """Test create_dataset returns a DatasetBuilder when base is a FeatureGroup."""
+        from sagemaker.mlops.feature_store.feature_utils import create_dataset
+
+        mock_builder = MagicMock()
+        mock_builder_class.create.return_value = mock_builder
+
+        mock_fg = MagicMock()
+        mock_session = MagicMock()
+
+        result = create_dataset(
+            base=mock_fg,
+            output_path="s3://bucket/output",
+            session=mock_session,
+        )
+
+        mock_builder_class.create.assert_called_once_with(
+            base=mock_fg,
+            output_path="s3://bucket/output",
+            session=mock_session,
+            record_identifier_feature_name=None,
+            event_time_identifier_feature_name=None,
+            included_feature_names=None,
+            kms_key_id=None,
+        )
+        assert result == mock_builder
+
+    @patch("sagemaker.mlops.feature_store.feature_utils.DatasetBuilder")
+    def test_create_dataset_with_dataframe_base(self, mock_builder_class):
+        """Test create_dataset returns DatasetBuilder when base is a DataFrame."""
+        from sagemaker.mlops.feature_store.feature_utils import create_dataset
+
+        mock_builder = MagicMock()
+        mock_builder_class.create.return_value = mock_builder
+
+        df = pd.DataFrame({"id": [1, 2], "ts": ["2024-01-01", "2024-01-02"]})
+        mock_session = MagicMock()
+
+        result = create_dataset(
+            base=df,
+            output_path="s3://bucket/output",
+            session=mock_session,
+            record_identifier_feature_name="id",
+            event_time_identifier_feature_name="ts",
+        )
+
+        mock_builder_class.create.assert_called_once_with(
+            base=df,
+            output_path="s3://bucket/output",
+            session=mock_session,
+            record_identifier_feature_name="id",
+            event_time_identifier_feature_name="ts",
+            included_feature_names=None,
+            kms_key_id=None,
+        )
+        assert result == mock_builder
+
+    @patch("sagemaker.mlops.feature_store.feature_utils.DatasetBuilder")
+    def test_create_dataset_with_dataframe_missing_required_params_raises(self, mock_builder_class):
+        """Test ValueError when base is DataFrame but required params are missing."""
+        from sagemaker.mlops.feature_store.feature_utils import create_dataset
+
+        # Make DatasetBuilder.create raise ValueError like the real implementation
+        mock_builder_class.create.side_effect = ValueError(
+            "record_identifier_feature_name and event_time_identifier_feature_name "
+            "are required when base is a DataFrame."
+        )
+
+        df = pd.DataFrame({"id": [1, 2], "ts": ["2024-01-01", "2024-01-02"]})
+        mock_session = MagicMock()
+
+        with pytest.raises(ValueError, match="record_identifier_feature_name"):
+            create_dataset(
+                base=df,
+                output_path="s3://bucket/output",
+                session=mock_session,
+            )
+
+    @patch("sagemaker.mlops.feature_store.feature_utils.DatasetBuilder")
+    def test_create_dataset_passes_all_params(self, mock_builder_class):
+        """Test that all parameters are forwarded to DatasetBuilder.create."""
+        from sagemaker.mlops.feature_store.feature_utils import create_dataset
+
+        mock_builder = MagicMock()
+        mock_builder_class.create.return_value = mock_builder
+
+        mock_fg = MagicMock()
+        mock_session = MagicMock()
+
+        result = create_dataset(
+            base=mock_fg,
+            output_path="s3://bucket/output",
+            session=mock_session,
+            record_identifier_feature_name="rec_id",
+            event_time_identifier_feature_name="event_ts",
+            included_feature_names=["feat1", "feat2"],
+            kms_key_id="my-kms-key",
+        )
+
+        mock_builder_class.create.assert_called_once_with(
+            base=mock_fg,
+            output_path="s3://bucket/output",
+            session=mock_session,
+            record_identifier_feature_name="rec_id",
+            event_time_identifier_feature_name="event_ts",
+            included_feature_names=["feat1", "feat2"],
+            kms_key_id="my-kms-key",
+        )
+        assert result == mock_builder