Google has released a Workbench Notebooks extension for VS Code, giving machine learning developers a more direct way to use Google Cloud compute from their local editor.

The extension connects VS Code notebooks to existing Workbench instances, which are managed Jupyter notebook environments on Google Cloud. Google says the goal is to reduce context switching between a local development setup and browser-based cloud notebooks, while still letting teams use larger remote compute when local machines are not enough.

The workflow is centered on the Jupyter extension. After installing the Workbench extension, a developer can open or create a notebook, choose a Google Cloud Workbench kernel, sign in, select a project, and connect to an active Workbench instance. The Marketplace listing describes the extension as exposing Workbench Jupyter servers directly inside VS Code.

The release is also open source. Google's public repository describes the project as a VS Code extension for Vertex AI Workbench, and the Marketplace page points users to GitHub issues and discussions for bugs and feature requests.

The practical importance is less about a new model capability and more about developer infrastructure. Managed notebook platforms are common in ML teams, but many developers prefer editing, terminals, keybindings, and extensions inside VS Code. By moving the kernel connection into the editor, Google is trying to make cloud-hosted ML work feel closer to a normal local workflow.

The constraint is that developers still need access to a Workbench instance and a Google Cloud project. This is a bridge into existing cloud infrastructure, not a standalone notebook service.