model_garden

Learn how to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.

The steps performed are:
* Setup the project.
* Download the prediction data of pretrain model onf Syn2 data.
* Visualize and understand the feature importance based on the masks output.
* Clean up the resource created by this tutorial.

Learn more about Tabular Workflow for TabNet.

Get started with TabNet builtin algorithm for training tabular models

Learn how to run `Vertex AI TabNet` built algorithm for training custom tabular models.

The steps performed include:

- Get the training data.
- Configure training parameters for the `Vertex AI TabNet` container.
- Train the model using `Vertex AI Training` using CSV data.
- Upload the model as a `Vertex AI Model` resource.
- Deploy the `Vertex AI Model` resource to a `Vertex AI Endpoint` resource.
- Make a prediction with the deployed model.
- Hyperparameter tuning the `Vertex AI TabNet` model.

Learn more about Tabular Workflow for TabNet.

Train a TabNet model using Vertex AI Remote Training with Vertex AI SDK 2.0

Learn to use `Vertex AI SDK 2.

The steps performed include:

- Download and split the dataset
- Ingest the data in a Dataframe and perform transformations.
- Train a tabular classification model.
- Train a tabular regression model.

Vertex AI TabNet

Learn how to run TabNet model on Vertex AI.

The steps performed are:
1. **Setup**: Importing the required libraries and setting your global variables.
2. **Configure parameters**: Setting the appropriate parameter values for the training job.
3. **Train on Vertex AI Training**: Submitting a training job using csv input.
4. **Hyperparameter tuning**: Running a hyperparameter tuning job.
5. **Hyperparameter on Vertex AI Training with BigQuery input**: Submitting a training job using BigQuery input.
6. **Cleaning up**: Deleting resources created by this tutorial.

Learn more about Tabular Workflow for TabNet.