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Miscellaneous editorial fixes and rebranding to "Vertex AI Feature Store (Legacy)" (#2358)

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* Miscellaneous editorial fixes and rebranding to "Vertex AI Feature Store (Legacy)"

* chore: rebrand Legacy Feature Store product
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sen-sam
2023-10-11 00:35:56 +00:00
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parent 8fede82c32
commit e4275197ac
2 changed files with 41 additions and 42 deletions
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notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb
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@@ -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",
notebooks/official/feature_store/sdk-feature-store.ipynb
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@@ -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."
]
},
{