fix: autogen index for official/ml_metadata (#1105)

* feat: add autogen index

* fix: missed the REAME

* fix: update autogen index

* feat: add autogen index

* feat: add autogen index

* feat: add autogen index

notebooks/official/ml_metadata/README.md

@@ -0,0 +1,30 @@
+
+[Track parameters and metrics for locally trained models](official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
+
+Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
+
+The steps performed include:
+
+- Track parameters and metrics for a locally trained model.
+- Extract and perform analysis for all parameters and metrics within an Experiment.
+
+[Track parameters and metrics for custom training jobs](official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
+
+Learn how to use Vertex AI SDK for Python to:
+
+The steps performed include:
+- Track training parameters and prediction metrics for a custom training job.
+- Extract and perform analysis for all parameters and metrics within an Experiment.
+
+[Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](official/ml_metadata/vertex-pipelines-ml-metadata.ipynb)
+
+Learn how to track artifacts and metrics with `Vertex ML Metadata` in `Vertex AI Pipeline` runs.
+
+The steps performed include:
+
+* Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI
+* The pipeline will create a dataset, train a scikit-learn model, and deploy the model to an endpoint
+* Write custom pipeline components that generate artifacts and metadata
+* Compare Vertex Pipelines runs, both in the Cloud console and programmatically
+* Trace the lineage for pipeline-generated artifacts
+* Query your pipeline run metadata