fix: rebrand

notebooks/community/ml_ops/stage6/get_started_with_matching_engine.ipynb

@@ -38,7 +38,7 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# E2E ML on GCP: MLOps stage 6 : serving: get started with Vertex AI Matching Engine\n",
+        "# E2E ML on GCP: MLOps stage 6 : serving: get started with Vertex AI Vector Search\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
@@ -70,7 +70,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use `Vertex AI Matching Engine` service. This Cloud AI service is a appropriamate nearest neighbor (ANN) index and matching service for vectors (i.e., embeddings), with high scaling and low latency.\n",
+        "This tutorial demonstrates how to use `Vertex AI Vector Search` service. This Cloud AI service is a appropriamate nearest neighbor (ANN) index and matching service for vectors (i.e., embeddings), with high scaling and low latency.\n",
         "the GCP ANN Service.  The service is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research.\n",
         "\n",
         "There are several levels of using this service.\n",
@@ -87,7 +87,7 @@
         "\n",
         "The user configures the serving binary how to generate embeddings from the model, indexing and querying, using `Vertex AI Explanations by Examples`\n",
         "\n",
-        "Learn more about [Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview)"
+        "Learn more about [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview)"
       ]
     },
     {
@@ -102,7 +102,7 @@
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",
-        "- `Vertex AI Matching Engine`\n",
+        "- `Vertex AI Vector Search`\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -574,7 +574,7 @@
         "### Download and prepare the prebuilt GloVe embeddings\n",
         "\n",
         "The GloVe embeddings consists of a set of pre-trained embeddings. The embeddings are split into a \"train\" and \"test\" splits.\n",
-        "You create a `Vertex AI Matching Engine` index from the \"train\" split, and use the embedding vectors in the \"test\" split as query vectors to test the index.\n",
+        "You create a `Vertex AI Vector Search` index from the \"train\" split, and use the embedding vectors in the \"test\" split as query vectors to test the index.\n",
         "\n",
         "*Note:* While the data split uses the term \"train\", these are pre-trained embeddings and thus are ready to be indexed for search. The terms \"train\" and \"test\" split are used just to be consistent with usual machine learning terminology."
       ]
@@ -676,7 +676,7 @@
         "id": "qhIBCQ7dDSbW"
       },
       "source": [
-        "### Create Matching Engine Index\n",
+        "### Create Vector Search Index\n",
         "\n",
         "Next, you create the index for your embeddings. Currently, two indexing algorithms are supported:\n",
         "\n",
@@ -701,7 +701,7 @@
         "\n",
         "This may take upto 30 minutes.\n",
         "\n",
-        "Learn more about [Configuring Matching Engine Indexes](https://cloud.google.com/vertex-ai/docs/matching-engine/configuring-indexes)."
+        "Learn more about [Configuring Vector Search Indexes](https://cloud.google.com/vertex-ai/docs/matching-engine/configuring-indexes)."
       ]
     },
     {
@@ -804,7 +804,7 @@
       "source": [
         "## Setup VPC peering network\n",
         "\n",
-        "To use a `Matching Engine Index`, you setup a VPC peering network between your project and the `Vertex AI Matching Engine` service project. This eliminates additional hops in network traffic and allows using efficient gRPC protocol.\n",
+        "To use a `Vector Search Index`, you setup a VPC peering network between your project and the `Vertex AI Vector Search` service project. This eliminates additional hops in network traffic and allows using efficient gRPC protocol.\n",
         "\n",
         "Learn more about [VPC peering](https://cloud.google.com/vertex-ai/docs/general/vpc-peering).\n",
         "\n",
@@ -909,7 +909,7 @@
       "source": [
         "#### Construct the full network name\n",
         "\n",
-        "You need to have the full network resource name when you subsequently create an `Matching Engine Index Endpoint` resource for VPC peering."
+        "You need to have the full network resource name when you subsequently create an `Vector Search Index Endpoint` resource for VPC peering."
       ]
     },
     {
@@ -931,7 +931,7 @@
       "source": [
         "### Create an IndexEndpoint with VPC Network\n",
         "\n",
-        "Next, you create a `Matching Engine Index Endpoint`, similar to the concept of creating a `Private Endpoint` for prediction with a peer-to-peer network.\n",
+        "Next, you create a `Vector Search Index Endpoint`, similar to the concept of creating a `Private Endpoint` for prediction with a peer-to-peer network.\n",
         "\n",
         "To create the `Index Endpoint` resource, you call the method `create()` with the following parameters:\n",
         "\n",
@@ -964,7 +964,7 @@
         "id": "8Ew1UgcIIiJG"
       },
       "source": [
-        "### Deploy the `Matching Engine Index` to the `Index Endpoint` resource\n",
+        "### Deploy the `Vector Search Index` to the `Index Endpoint` resource\n",
         "\n",
         "Next, deploy your index to the `Index Endpoint` using the method `deploy_index()` with the following parameters:\n",
         "\n",