This document contains a list of all the Vertex AI Jupyter Notebook tutorials. They are end-to-end tutorials that show you how to preprocess data, train, deploy, and use the models for inference. If you are new to Vertex AI, we recommend you start with Introduction to Vertex AI.
There are many environments in which you can host Jupyter Notebooks. You can run them on your local machine, a Jupyter or JupyterLab server in your local network or in the cloud using a service like Colaboratory (Colab) or Vertex AI Workbench.
Running a Jupyter Notebook in Colab is an easy way to get started quickly. All you need to do is click the Colab link in the following table. Colab spins up a VM instance with all needed dependencies, launches the Colab environment and loads the Notebook. At this point the notebook is ready to run.
You can also run the notebook using user-managed notebooks. When you create a user-managed notebooks instance with Vertex AI Workbench, you have full control over the hosting VM. You can specify the hosting VM's machine configuration and environment. To open a notebook sample in a Vertex AI Workbench user-managed notebooks instance, click the Vertex AI Workbench link in the following table. The link opens the Vertex AI Workbench console. In the Deploy to notebook screen, type a name for your new notebook instance and select CREATE. After the notebook instance has started a Ready to open notebook dialog is displayed. Select OPEN. On the Confirm deployment to notebook server page, select Confirm. Before running the notebook, select Kernel > Restart Kernel and Clear all Outputs. The notebook is now ready to run.
| Vertex AI feature | Notebook | Description | Open in |
|---|---|---|---|
| AutoML | Text classification model | Create, train and deploy a text classification model on Vertex AI. | |
| AutoML | Time-series forecasting model | Create, train, and use an AutoML time-series forecasting model for batch prediction. | |
| Custom training | Batch prediction | Use the Vertex AI SDK for Python to train and deploy a custom image classification model for batch prediction. | |
| Custom training | Online prediction | Use the Vertex AI SDK for Python to train and deploy a custom image classification model for online prediction. | |
| Vertex Explainable AI | Tabular binary classification for batch prediction | Use the Vertex AI SDK to create tabular binary classification models and perform batch prediction with explanation using a AutoML model. | |
| Vertex Explainable AI | Tabular binary classification for online prediction | Use the Vertex AI SDK to create tabular classification models and do online prediction with explanation using a AutoML model. | |
| Vertex Explainable AI | Custom training image classification model for batch prediction | Use the Vertex AI SDK to train and deploy a custom image classification model for batch prediction with explanation. | |
| Vertex Explainable AI | Custom training image classification model for online prediction | Use the Vertex AI SDK to train and deploy a custom image classification model for online prediction with explanation. | |
| Vertex Explainable AI | Training tabular regression models for batch prediction | Use the Vertex AI SDK to train and deploy a custom tabular regression model for batch prediction with explanation. | |
| Vertex Explainable AI | Custom training tabular regression model for online prediction | Use the Vertex AI SDK to train and deploy a custom tabular regression model for online prediction with explanation. | |
| Vertex AI Feature Store | Manage sample ML features using Vertex AI Feature Store | Store, serve, manage, and share machine learning features at a large scale. | |
| Model Monitoring | Use Model Monitoring to detect drift and training-prediction skew | Interpret statistics, visualizations, and other data reported by Model Monitoring. | |
| Vertex ML Metadata | Custom training job parameter and metric tracking | Track metrics and parameters for Vertex AI custom training jobs, and perform detailed analysis. | |
| Vertex ML Metadata | Locally trained model parameter and metric tracking | Track metrics and parameters for ML training jobs and analyze this metadata using Vertex AI SDK. | |
| Vertex AI Pipelines | Create a pipeline with lightweight components based on Python functions | Use the Kubeflow Pipelines (KFP) SDK to build Vertex AI Pipelines. | |
| Vertex AI Pipelines | AutoML image classification model workflow | Build an AutoML image classification workflow on Vertex AI Pipelines. | |
| Vertex AI Pipelines | AutoML tabular classification model workflow | Build an AutoML tabular classification workflow on Vertex AI Pipelines. | |
| Vertex AI Pipelines | AutoML tabular regression model workflow | Build an AutoML tabular regression workflow on Vertex AI Pipelines. | |
| Vertex AI Pipelines | AutoML text classification model workflow | Build an AutoML text classification workflow on Vertex AI Pipelines. | |
| Vertex AI Pipelines | Custom training with pre-built Google Cloud Pipeline Components | Use Vertex AI Pipelines with pre-built Google Cloud Pipeline Components for custom training. | |
| Vertex AI Pipelines | Custom training workflow with pre-built Google Cloud Pipeline Components and custom components | Use custom components to build a Vertex AI Pipelines workflow. | |
| Vertex AI Pipelines | Pipelines with control structures | Use the Kubeflow Pipelines (KFP) SDK to build Vertex AI Pipelines that use control structures. | |
| Vertex AI Pipelines | Model metrics | Build Vertex AI Pipelines that generate model metrics and visualizations, and compare pipeline runs. | |
| Vertex AI Pipelines | Vertex AI Pipelines and the Kubeflow Pipelines SDK | Use Vertex AI Pipelines with the Kubeflow Pipelines (KFP) SDK. | |
| Vertex AI Vizier | Multi-objective optimization | Optimize multiple objective functions simultaneously. |