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**REQUIRED:** Add a summary of your PR here, typically including why the change is needed and what was changed. Include any design alternatives for discussion purposes. <br> 1. Boilerplate changes. 2. Added code for deleting experiment otherwise its throwing experiment name already exist. <br><br><br> **REQUIRED:** Fill out the below checklists or remove if irrelevant 1. If you are opening a PR for `Official Notebooks` under the [notebooks/official](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official) folder, follow this mandatory checklist: - [X] Use the [notebook template](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_template.ipynb) as a starting point. - [X] Follow the style and grammar rules outlined in the above notebook template. - [X] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes. - [X] Passes all the required automated checks. You can locally test for formatting and linting with these [instructions](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/CONTRIBUTING.md#code-quality-checks). - [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it. - [X] This notebook has been added to the [CODEOWNERS](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/CODEOWNERS) file under the `Official Notebooks` section, pointing to the author or the author's team. - [X] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources. <br>
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.
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.
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