Update README.md (7) (#3223)

README.md

@@ -53,63 +53,108 @@ To get started using Vertex AI, you must have a Google Cloud project.
 <table>
 
   <tr>
-    <th></th>
+    <th style="text-align: center;">Category</th>
+    <th style="text-align: center;">Product</th>
     <th style="text-align: center;">Description</th>
-    <th style="text-align: center;">Contents</th>
   </tr>
   <tr>
+    <td>Model</td>
+    <td>
+      <a href="notebooks/community/model_garden"><code>Model Garden/</code></a>
+    </td>
+    <td>
+      Curated collection of first-party, open-source, and third-party models available on Vertex AI including Gemini, Gemma, Llama 3, Claude 3 and many more.
+    </td>
+  </tr>
+  <tr>
+    <td>Data</td>
+    <td>
+      <a href="notebooks/official/feature_store"><code>Feature Store/</code></a>
+    </td>
+    <td>
+      Set up and manage online serving using Vertex AI Feature Store.
+    </td>
+  </tr>
+  <tr>
+    <td></td>
+    <td>
+      <a href="notebooks/official/datasets"><code>datasets/</code></a>
+    </td>
+    <td>
+      Use BigQuery and Data Labeling service with Vertex AI.
+    </td>
+  </tr>
+  <tr>
+    <td>Model development</td>
     <td>
       <a href="notebooks/official/automl"><code>automl/</code></a>
     </td>
     <td>
       Train and make predictions on AutoML models
     </td>
-    <td><a href="notebooks/official/automl/">Sample notebooks</a></td>
   </tr>
   <tr>
+    <td></td>
     <td>
       <a href="notebooks/official/custom"><code>custom/</code></a>
     </td>
     <td>
       Create, deploy and serve custom  models on Vertex AI
     </td>
-    <td><a href="notebooks/official/custom/">Sample notebooks</a></td>
   </tr>
   <tr>
+    <td></td>
+    <td>
+      <a href="notebooks/official/ray_on_vertex_ai"><code>ray_on_vertex_ai/</code></a>
+    </td>
+    <td>
+      Use Colab Enterprise and Vertex AI SDK for Python to connect to the Ray Cluster.
+    </td>
+  </tr>
+  <tr>
+    <td>Deploy and use</td>
     <td>
       <a href="notebooks/official/prediction"><code>prediction/</code></a>
     </td>
     <td>
       Build, train and deploy models using prebuilt containers for custom training and prediction.
     </td>
-    <td><a href="notebooks/official/prediction/">Sample notebooks</a></td>
   </tr>
   <tr>
+    <td></td>
     <td>
-      <a href="notebooks/official/feature_store"><code>Feature Store/</code></a>
+      <a href="notebooks/official/model_registry"><code>model_registry/</code></a>
     </td>
     <td>
-      Set up and manage online serving using Vertex AI Feature Store.
+      Use Model Registry to create and register a model.
     </td>
-    <td><a href="notebooks/official/feature_store/">Sample notebooks</a></td>
   </tr>
   <tr>
+    <td></td>
     <td>
       <a href="notebooks/official/explainable_ai"><code>Explainable AI/</code></a>
     </td>
     <td>
       Use Vertex Explainable AI's feature-based and example-based explanations to explain how or why a model produced a specific prediction.
     </td>
-    <td><a href="notebooks/official/explainable_ai/">Sample notebooks</a></td>
   </tr>
   <tr>
+    <td></td>
+    <td>
+      <a href="notebooks/official/ml_metadata"><code>ml_metadata/</code></a>
+    </td>
+    <td>
+      Record the metadata and artifacts and query that metadata to help analyze, debug, and audit the performance of your ML system.
+    </td>
+  </tr>
+  <tr>
+    <td>Tools</td>
     <td>
       <a href="notebooks/official/pipelines"><code>Pipelines/</code></a>
     </td>
     <td>
       Use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build, tune, or deploy a custom model.
     </td>
-    <td><a href="notebooks/official/pipeline/">Sample notebooks</a></td>
   </tr>
 </table>
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