Workspaces

Steamship Workspaces manage the data, models, and infrastructure necessary for a language AI project.

Workspaces can be used in two ways:

1. As a cloud environment for language AI projects. You can use Workspaces on their own from Jupyter notebooks or your own application code.

2. As the backing store to :ref:`Steamship Packages<Packages>`. Each Steamship Package instance is bound to a Workspace, giving it an isolated environment to store state and model parameters.

The following sections cover the core lifecycle and data model of a Workspace.

• Creating Workspaces
• Importing Data
  • Import content directly
  • Import content via a Plugin
• Blockifying Data
• Workspace Data Model
  • Files
  • Blocks
  • Tags
• Querying Data
  • Usage
  • Language Description