Vertex AI Workbench is a single development environment for the entire data science workflow.
You can use Vertex AI Workbench's notebook-based environment to query and explore data, develop and train a model, and run your code as part of a pipeline.
For example, Vertex AI Workbench lets you:
- Access and explore your data from within a Jupyter notebook by using BigQuery and Cloud Storage integrations.
- Automate recurring updates to your model by using scheduled executions of your notebook's code that run on Vertex AI.
- Process data quickly by running a notebook on a Dataproc cluster.
- Run a notebook as a step in a pipeline by using Vertex AI Pipelines.
Vertex AI Workbench offers a managed notebooks option with built-in integrations that help you to set up an end-to-end notebook-based production environment. For users who need full control over their environment, Vertex AI Workbench provides a user-managed notebooks option.
Both notebook options are prepackaged with JupyterLab and have a preinstalled suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can use CPU-only or GPU-enabled instances. Vertex AI Workbench notebook instances also integrate with GitHub so that you can sync your notebook with a GitHub repository.
Your Vertex AI Workbench notebook instances are protected by Google Cloud authentication and authorization.