model_garden/notebooks/official/ml_metadata

Track parameters and metrics for locally trained models

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.

   Learn more about Vertex ML Metadata.

Track parameters and metrics for custom training jobs

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.

   Learn more about Vertex ML Metadata.

   Learn more about Vertex AI Training.

Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata

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

   Learn more about Vertex ML Metadata.

   Learn more about Vertex AI Pipelines.