Tutorials overview

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Each of the tutorials presented here walks you through a specific artificial intelligence (AI) workflow, created to represent the most common tasks and to illustrate the capabilities of Vertex AI. Choose the tutorial that best matches your data type and AI task. After following the tutorial, you can use the patterns that you have learned to solve your own AI problem. Vertex AI offers Google Cloud console tutorials and Jupyter notebook tutorials that use the Python SDK. You can open a notebook tutorial directly in Colab, download the notebook to your preferred environment, or open the notebook tutorial in Vertex AI Workbench.

Train a classification model for tabular data

Tabular classification training introduction

Create a Vertex AI dataset from tabular data, and then train a classification model with AutoML. Deploy the model to an endpoint and make online predictions.

Google Cloud console: You can choose tutorial guides with step-by-step instructions for the Google Cloud console.
Show on cloud.google.com | Show in an interactive format in Google Cloud console

Jupyter notebook: You can choose to run this tutorial as a Jupyter notebook.
Run in Colab | View on GitHub | Open in Vertex AI Workbench

Train a regression model for tabular data

Tabular regression training introduction

Create a Vertex AI dataset from tabular data, and then train a regression model with AutoML. Deploy the model to an endpoint and make online predictions or make predictions in batch format.

Jupyter notebook: You can choose to run this tutorial and make online predictions using a Jupyter notebook.
Run in Colab | View on GitHub | Open in Vertex AI Workbench

Jupyter notebook: You can choose to run this tutorial and make batch predictions using a Jupyter notebook.
Run in Colab | View on GitHub | Open in Vertex AI Workbench

Train a time-series forecasting model for tabular data

Tabular forecasting training introduction

Create a Vertex AI dataset from tabular data, and then train a forecasting model with AutoML. Make predictions in batch format.

Jupyter notebook: You can choose to run this tutorial as a Jupyter notebook.
Run in Colab | View on GitHub | Open in Vertex AI Workbench

Train a classification model for text data

Text classification training introduction

Create a Vertex AI dataset for text data, and then train a classification model with AutoML. Deploy the model to an endpoint and make online predictions.

Google Cloud console: You can choose tutorial guides with step-by-step instructions for the Google Cloud console.
Show on cloud.google.com | Show in an interactive format in Google Cloud console

Jupyter notebook: You can choose to run this tutorial as a Jupyter notebook.
Run in Colab | View on GitHub | Open in Vertex AI Workbench

Train a classification model for image data

Image classification training introduction

Create a Vertex AI dataset for image data, and then train a classification model with AutoML. Deploy the model to an endpoint and make online predictions.

Google Cloud console: You can choose tutorial guides with step-by-step instructions for the Google Cloud console.
Show on cloud.google.com | Show in an interactive format in Google Cloud console

Train a classification model for video data

Video classification training introduction

Create a Vertex AI dataset for video data, and then train a classification model with AutoML. Make predictions in batch format.

Google Cloud console: You can choose tutorial guides with step-by-step instructions for the Google Cloud console.
Show on cloud.google.com | Show in an interactive format in Google Cloud console

Jupyter notebook: You can choose to run this tutorial as a Jupyter notebook.
Run in Colab | View on GitHub | Open in Vertex AI Workbench

How to open a notebook in Vertex AI Workbench

To open a notebook sample in a Vertex AI Workbench instance:

  1. Click the Vertex AI Workbench link in the notebook list. The link opens the Vertex AI Workbench console.
  2. In the Deploy to notebook screen, type a name for your new notebook instance and click Create.
  3. After the notebook instance has started, a Ready to open notebook dialog is displayed. Click Open.
  4. On the Confirm deployment to notebook server page, select Confirm.
  5. Before running the notebook, select Kernel > Restart Kernel and Clear all Outputs.

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