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Patch-4 (#2363)
* Miscellaneous editorial fixes and rebranding to "Vertex AI Feature Store (Legacy)" * chore: rebrand Legacy Feature Store product * chore: Rebrand to "Vertex AI Feature Store (Legacy)" and change "ingest" to "import". --------- Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>
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sen-sam
co-authored by
Andrew Ferlitsch
Andrew Ferlitsch
parent
71f3f12607
commit
c6f451c8d1
@@ -29,7 +29,7 @@
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"id": "JAPoU8Sm5E6e"
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},
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"source": [
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"# Vertex AI Feature Store (Legacy): Streaming ingestion SDK\n",
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"# Vertex AI Feature Store (Legacy): Streaming import SDK\n",
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"\n",
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"<table align=\"left\">\n",
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"\n",
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@@ -72,7 +72,7 @@
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"source": [
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"## Overview\n",
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"\n",
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"This notebook demonstrates how to use streaming ingestion at the SDK layer in Vertex AI Feature Store (Legacy).\n",
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"This notebook demonstrates how to use streaming import at the SDK layer in Vertex AI Feature Store (Legacy).\n",
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"\n",
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"Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore)."
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]
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@@ -99,7 +99,7 @@
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"- Read entity feature values from the online feature store into Pandas DataFrame.\n",
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"- Batch serve feature values from your featurestore into Pandas DataFrame.\n",
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"\n",
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"You also learn how Vertex AI Feature Store (Legacy) is useful in the following scenarios:\n",
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"You also learn how Vertex AI Feature Store (Legacy) is useful in the below scenarios:\n",
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"\n",
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"- Online serving with updated feature values.\n",
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"- Point-in-time correctness to fetch feature values for training."
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@@ -450,11 +450,11 @@
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"source": [
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"## Create entity types\n",
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"\n",
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"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",
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"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",
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"\n",
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"Learn more about [entity types](https://cloud.google.com/vertex-ai/docs/featurestore/concepts#entity_type).\n",
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"\n",
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"Entity types are created within the `Featurestore` class. Below, you create the following entity types `users` and `movies` for the movie recommendation dataset.\n",
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"Entity types are created within the `Featurestore class. Below, you create the following entity types `users` and `movies` for the movie recommendation dataset.\n",
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"\n",
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"You pass the following parameters while creating the entity types:\n",
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"\n",
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@@ -736,7 +736,7 @@
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},
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"outputs": [],
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"source": [
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"# ingest the data for movies\n",
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"# Import the data for movies\n",
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"movies_entity_type.ingest_from_df(\n",
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" feature_ids=[\"average_rating\", \"title\", \"genres\"],\n",
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" feature_time=\"update_time\",\n",
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@@ -900,7 +900,7 @@
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"\n",
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"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",
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"\n",
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"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). "
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"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). "
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]
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},
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{
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@@ -1080,7 +1080,7 @@
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"source": [
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"### Import the backfilled / corrected data\n",
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"\n",
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"Import the imputed point-in-time data from dataframe to the entity types in featurestore."
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"Import the imputed point-in-time data from dataframe to the entity types in the featureImpostore."
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]
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},
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{
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@@ -87,7 +87,7 @@
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"- Import feature data into the `Featurestore` resource.\n",
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"- Serve online prediction requests using the imported features.\n",
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"- Access imported features in offline jobs, such as training jobs.\n",
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"- Use streaming import to import small amounts of data."
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"- Use streaming import to import small amount of data."
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]
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},
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{
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@@ -1252,7 +1252,7 @@
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},
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"outputs": [],
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"source": [
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"# Call `write_feature_values` to import data to the `users` entity type.\n",
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"# Call `write_feature_values` to import data to `users` entity type.\n",
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"data_client.write_feature_values(\n",
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" entity_type=admin_client.entity_type_path(\n",
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" PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n",
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@@ -1295,7 +1295,6 @@
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},
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"source": [
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"Upon successful completion, the `write_feature_values` API returns an empty response.\n",
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"\n",
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"Similarly, import data to the `movies` entity type."
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]
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},
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