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 6a2cd78ed..9a6ec7f53 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": [ - "# Vertex AI Feature Store (Legacy): Streaming ingestion SDK\n", + "# Vertex AI Feature Store (Legacy): Streaming import SDK\n", "\n", "\n", "\n", @@ -72,7 +72,7 @@ "source": [ "## Overview\n", "\n", - "This notebook demonstrates how to use streaming ingestion at the SDK layer in Vertex AI Feature Store (Legacy).\n", + "This notebook demonstrates how to use streaming import 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)." ] diff --git a/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb b/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb index 5552d4a6b..e14c8ff61 100644 --- a/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb +++ b/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb @@ -99,7 +99,7 @@ "- Read entity feature values from the online feature store into Pandas DataFrame.\n", "- Batch serve feature values from your featurestore into Pandas DataFrame.\n", "\n", - "You also learn how Vertex AI Feature Store (Legacy) is useful in the following scenarios:\n", + "You also learn how Vertex AI Feature Store (Legacy) is useful in the below scenarios:\n", "\n", "- Online serving with updated feature values.\n", "- Point-in-time correctness to fetch feature values for training." @@ -450,11 +450,11 @@ "source": [ "## Create entity types\n", "\n", - "Using Vertex AI Feature Store (Legacy), you can create and manage featurestores, entity types, and features. 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 customers.\n", + "Using Vertex AI Feature Store (Legacy), you can create and manage feature stores, entity types, and features. 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 customers.\n", "\n", "Learn more about [entity types](https://cloud.google.com/vertex-ai/docs/featurestore/concepts#entity_type).\n", "\n", - "Entity types are created within the `Featurestore` class. Below, you create the following entity types `users` and `movies` for the movie recommendation dataset.\n", + "Entity types are created within the `Featurestore class. Below, you create the following entity types `users` and `movies` for the movie recommendation dataset.\n", "\n", "You pass the following parameters while creating the entity types:\n", "\n", @@ -736,7 +736,7 @@ }, "outputs": [], "source": [ - "# ingest the data for movies\n", + "# Import the data for movies\n", "movies_entity_type.ingest_from_df(\n", " feature_ids=[\"average_rating\", \"title\", \"genres\"],\n", " feature_time=\"update_time\",\n", @@ -900,7 +900,7 @@ "\n", "Note: Calling the `batch_serve_to_df` method automatically creates and deletes a temporary bigquery dataset in the same GCP project, which is used as the intermediary storage for batch serve feature values from Vertex AI Feature Store (Legacy) to dataframe.\n", "\n", - "Learn more about [batch serving from Vertex AI Feature Store (Legacy) to a dataframe](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.Featurestore#google_cloud_aiplatform_Featurestore_batch_serve_to_df). " + "Learn more about [Batch serving from Vertex AI Feature Store (Legacy) to a dataframe](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.Featurestore#google_cloud_aiplatform_Featurestore_batch_serve_to_df). " ] }, { @@ -1080,7 +1080,7 @@ "source": [ "### Import the backfilled / corrected data\n", "\n", - "Import the imputed point-in-time data from dataframe to the entity types in featurestore." + "Import the imputed point-in-time data from dataframe to the entity types in the featureImpostore." ] }, { diff --git a/notebooks/official/feature_store/sdk-feature-store.ipynb b/notebooks/official/feature_store/sdk-feature-store.ipynb index dde0c4dc3..3777d233f 100644 --- a/notebooks/official/feature_store/sdk-feature-store.ipynb +++ b/notebooks/official/feature_store/sdk-feature-store.ipynb @@ -87,7 +87,7 @@ "- 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 import to import small amounts of data." + "- Use streaming import to import small amount of data." ] }, { @@ -1252,7 +1252,7 @@ }, "outputs": [], "source": [ - "# Call `write_feature_values` to import data to the `users` entity type.\n", + "# Call `write_feature_values` to import data to `users` entity type.\n", "data_client.write_feature_values(\n", " entity_type=admin_client.entity_type_path(\n", " PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n", @@ -1295,7 +1295,6 @@ }, "source": [ "Upon successful completion, the `write_feature_values` API returns an empty response.\n", - "\n", "Similarly, import data to the `movies` entity type." ] },