diff --git a/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb b/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb
index 31b4fbe4b..6aceca68c 100644
--- a/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb
+++ b/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb
@@ -29,7 +29,7 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Feature Store: Streaming ingestion SDK\n",
+        "# Vertex AI Feature Store (Legacy): Streaming ingestion SDK\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
@@ -72,7 +72,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates how to use Vertex AI Feature Store's streaming ingestion at the SDK layer.\n",
+        "This notebook demonstrates how to use streaming ingestion at the SDK layer in Vertex AI Feature Store (Legacy).\n",
         "\n",
         "Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore)."
       ]
@@ -85,18 +85,18 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to ingest features from a `Pandas DataFrame` into your Vertex AI Feature Store using `write_feature_values` method from the Vertex AI SDK.\n",
+        "In this tutorial, you learn how to ingest features from a `Pandas DataFrame` into Vertex AI Feature Store (Legacy) using `write_feature_values` method from the Vertex AI SDK.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services and resources:\n",
         "\n",
-        "- Vertex AI Feature Store\n",
+        "- Vertex AI Feature Store (Legacy)\n",
         "\n",
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create `Feature Store`\n",
-        "- Create new `Entity Type` for your `Feature Store`\n",
-        "- Ingest feature values from `Pandas DataFrame` into `Feature Store`'s `Entity Types`."
+        "- Create a featurestore.\n",
+        "- Create a new entity type for your featurestore.\n",
+        "- Import feature values from `Pandas DataFrame` into the entity type in the featurestore."
       ]
     },
     {
@@ -455,7 +455,7 @@
       "source": [
         "### Prepare the data\n",
         "\n",
-        "Feature values to be written to the Feature Store can take the form of a list of `WriteFeatureValuesPayload` objects, a Python `dict` of the form\n",
+        "Feature values to be written to the featurestore can take the form of a list of `WriteFeatureValuesPayload` objects, a Python `dict` of the form\n",
         "\n",
         "`{entity_id : {feature_id : feature_value}, ...},`\n",
         "\n",
@@ -493,13 +493,13 @@
         "id": "vgn4oQmSqdKI"
       },
       "source": [
-        "## Create Feature Store and define schemas\n",
+        "## Create featurestore and define schemas\n",
         "\n",
-        "Vertex AI Feature Store organizes resources hierarchically in the following order:\n",
+        "Vertex AI Feature Store (Legacy) organizes resources hierarchically in the following order:\n",
         "\n",
         "`Featurestore -> EntityType -> Feature`\n",
         "\n",
-        "You must create these resources before you can ingest data into Vertex AI Feature Store.\n",
+        "You must create these resources before you can import data into Vertex AI Feature Store (Legacy).\n",
         "\n",
         "Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore)"
       ]
@@ -510,14 +510,14 @@
         "id": "yaHwdbGjZWTq"
       },
       "source": [
-        "### Create a Feature Store\n",
+        "### Create a featurestore\n",
         "\n",
-        "You create a Feature Store using `aiplatform.Featurestore.create` with the following parameters:\n",
+        "You create a featurestore using `aiplatform.Featurestore.create` with the following parameters:\n",
         "\n",
-        "* `featurestore_id (str)`: The ID to use for this Featurestore, which will become the final component of the Featurestore's resource name. The value must be unique within the project and location.\n",
+        "* `featurestore_id (str)`: The ID to use for this featurestore, which will become the final component of the `featurestore` resource name. The value must be unique within the project and location.\n",
         "* `online_store_fixed_node_count`: Configuration for online serving resources.\n",
-        "* `project`: Project to create EntityType in. If not set, project set in `aiplatform.init` is used.\n",
-        "* `location`: Location to create EntityType in. If not set, location set in `aiplatform.init` is used.\n",
+        "* `project`: Project to create the `EntityType` in. If not set, project set in `aiplatform.init` is used.\n",
+        "* `location`: Location to create the `EntityType` in. If not set, location set in `aiplatform.init` is used.\n",
         "* `sync`:  Whether to execute this creation synchronously."
       ]
     },
@@ -546,8 +546,8 @@
         "id": "UfXgSD1VdzKb"
       },
       "source": [
-        "##### Verify that the Feature Store is created\n",
-        "Check if the Feature Store was successfully created by running the following code block."
+        "##### Verify that the featurestore is created\n",
+        "Check if the featurestore was successfully created by running the following code block."
       ]
     },
     {
@@ -572,13 +572,13 @@
         "id": "ep74rSlJWF3c"
       },
       "source": [
-        "### Create an EntityType\n",
+        "### Create an entity type\n",
         "\n",
         "An entity type is a collection of semantically related features. You define your own entity types, based on the concepts that are relevant to your use case. For example, a movie service might have the entity types `movie` and `user`, which group related features that correspond to movies or users.\n",
         "\n",
         "Here, you create an entity type entity type named `penguin_entity_type` using `create_entity_type` with the following parameters:\n",
-        "* `entity_type_id (str)`: The ID to use for the EntityType, which will become the final component of the EntityType's resource name. The value must be unique within a Feature Store.\n",
-        "* `description`: Description of the EntityType."
+        "* `entity_type_id (str)`: The ID to use for the `EntityType`, which will become the final component of the `EntityType` resource name. The value must be unique within a featurestore.\n",
+        "* `description`: Description of the `EntityType`."
       ]
     },
     {
@@ -604,8 +604,8 @@
         "id": "CquSdTp7duVw"
       },
       "source": [
-        "##### Verify that the EntityType is created\n",
-        "Check if the Entity Type was successfully created by running the following code block."
+        "##### Verify that the entity type is created\n",
+        "Check if the entity type was successfully created by running the following code block."
       ]
     },
     {
@@ -627,7 +627,7 @@
         "id": "2vYV2UUFehwZ"
       },
       "source": [
-        "### Create Features\n",
+        "### Create features\n",
         "A feature is a measurable property or attribute of an entity type. For example, `penguin` entity type has features such as `flipper_length_mm`, and `body_mass_g`. Features can be created within each entity type.\n",
         "\n",
         "When you create a feature, you specify its value type such as `DOUBLE`, and `STRING`. This value determines what value types you can ingest for a particular feature.\n",
@@ -692,10 +692,10 @@
         "id": "WBx26pZItUN4"
       },
       "source": [
-        "### Write features to the Feature Store\n",
-        "Use the `write_feature_values` API to write a feature to the Feature Store with the following parameter:\n",
+        "### Write features to the featurestore\n",
+        "Use the `write_feature_values` API to write a feature to the featurestore with the following parameter:\n",
         "\n",
-        "* `instances`: Feature values to be written to the Feature Store that can take the form of a list of WriteFeatureValuesPayload objects, a Python dict, or a pandas Dataframe.\n",
+        "* `instances`: Feature values to be written to the featurestore that can take the form of a list of `WriteFeatureValuesPayload` objects, a Python dict, or a pandas Dataframe.\n",
         "\n",
         "This streaming ingestion feature has been introduced to the Vertex AI SDK under the **preview** namespace. Here, you pass the pandas `Dataframe` you created from penguins dataset as `instances` parameter.\n",
         "\n",
diff --git a/notebooks/official/feature_store/sdk-feature-store.ipynb b/notebooks/official/feature_store/sdk-feature-store.ipynb
index 66257cf4c..ed27b5f28 100644
--- a/notebooks/official/feature_store/sdk-feature-store.ipynb
+++ b/notebooks/official/feature_store/sdk-feature-store.ipynb
@@ -29,7 +29,7 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Online and Batch predictions using Vertex AI Feature Store\n",
+        "# Online and Batch predictions using Vertex AI Feature Store (Legacy)\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -60,7 +60,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook introduces Vertex AI Feature Store, a managed cloud service for machine learning engineers and data scientists to store, serve, manage and share machine learning features at a large scale.\n",
+        "This notebook introduces Vertex AI Feature Store (Legacy), a managed cloud service for machine learning engineers and data scientists to store, serve, manage and share machine learning features at a large scale.\n",
         "\n",
         "This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\n",
         "\n",
@@ -75,7 +75,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this notebook, you learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.\n",
+        "In this notebook, you learn how to use `Vertex AI Feature Store (Legacy)` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
@@ -83,8 +83,8 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Create featurestore, entity type, and feature resources.\n",
-        "- Import feature data into `Vertex AI Feature Store` resource.\n",
+        "- Create `Featurestore`, `EntityType`, and `Feature` resources.\n",
+        "- Import feature data into the `Featurestore` resource.\n",
         "- Serve online prediction requests using the imported features.\n",
         "- Access imported features in offline jobs, such as training jobs.\n",
         "- Use streaming ingestion to ingest small amount of data."
@@ -406,11 +406,11 @@
       "source": [
         "## Terminology and concept\n",
         "\n",
-        "### Featurestore data model\n",
+        "### Vertex AI Feature Store (Legacy) data model\n",
         "\n",
-        "Vertex AI Feature Store organizes data with the following 3 important hierarchical concepts:\n",
+        "Vertex AI Feature Store (Legacy) organizes data with the following 3 important hierarchical concepts:\n",
         "```\n",
-        "Featurestore -> Entity type -> Feature\n",
+        "Featurestore -> EntityType -> Feature\n",
         "```\n",
         "* **Featurestore**: The place to store your features\n",
         "* **Entity type**: Under a featurestore, an entity type describes an object to be modeled, real one or virtual one.\n",
@@ -502,7 +502,7 @@
         "id": "EpmJq75zXjmT"
       },
       "source": [
-        "### Create entity Type\n",
+        "### Create entity type\n",
         "\n",
         "Entity types can be created within the `Featurestore` class. Below, create the `users` and `movies` entity types. A process log is printed out."
       ]
@@ -543,7 +543,7 @@
         "id": "G0TS9i5SJnkt"
       },
       "source": [
-        "To retrieve an entity type or check that it has been created use the [get_entity_type](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/featurestore/featurestore.py#L106) or [list_entity_types](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/featurestore/featurestore.py#L278) methods on the Featurestore object.\n"
+        "To retrieve an entity type or check that it has been created use the [get_entity_type](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/featurestore/featurestore.py#L106) or [list_entity_types](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/featurestore/featurestore.py#L278) methods on the `Featurestore` object.\n"
       ]
     },
     {
@@ -681,7 +681,7 @@
         "\n",
         "**Example of using the `search` method**\n",
         "\n",
-        "Use the following code snippet to search for all features within a feature store:\n"
+        "Use the following code snippet to search for all features within a featurestore:\n"
       ]
     },
     {
@@ -1034,7 +1034,7 @@
       "source": [
         "## Get batch predictions from your model\n",
         "\n",
-        "Batch serving is used to fetch a large batch of feature values for high-throughput, and is typically used for training a model or batch prediction. In this section, you learn how to prepare for training examples by using the Featurestore's batch serve function."
+        "Batch serving is used to fetch a large batch of feature values for high-throughput, and is typically used for training a model or batch prediction. In this section, you learn how to prepare for training examples by using the batch serve function in Vertex AI Feature Store (Legacy)."
       ]
     },
     {
@@ -1072,8 +1072,7 @@
         "-------------------- | ----------------- | --------------- | ---------------- | -------------------- | - | -------- | --------- | -----\n",
         "2019-11-01T00:00:00Z | bob              | 35        | M                | [Action, Crime]      | movie_02            | The Shining | Horror | 4.8\n",
         "2019-11-01T00:00:00Z | alice             | 55        | F                | [Drama, Comedy]      | movie_03          | Cinema Paradiso | Romance | 4.5 |\n",
-        "... | ... | ... | ... | ... | ... | ... | ... | ...\n",
-        ""
+        "... | ... | ... | ... | ... | ... | ... | ... | ...\n"
       ]
     },
     {
@@ -1298,7 +1297,7 @@
       },
       "source": [
         "Upon successful completion, the `write_feature_values` API returns an empty response.\n",
-        "Similarly, ingest data to the `movies` entity type"
+        "Similarly, ingest data to the `movies` entity type."
       ]
     },
     {