Assets in deployment spaces

Learn about various ways of adding and promoting assets to a space to support Watson Machine Learning deployments. Find the list of asset types that you can add to a space.

Note these considerations for importing assets into a space:

Upon import, each asset is automatically assigned a version number, starting with version 1. This to prevent overwriting the asset if you import an updated version later.
Supporting assets or references required to run jobs in the space must be part of the import package, or added separately, or the jobs will fail.

The way to add an asset to a space depends on the asset type. In case of some assets, you can add them directly to a space (for example a model that was created outside of Cloud Pak for Data). Other asset types originate in a project and have to be transferred from a project to a space. Third class are asset types that you can only add to a space as a dependency of another asset (these asset types are not directly exposed in the Assets tab in the UI). For details, refer to these sections:

Asset types that you can directly add to a space
Asset types that are created in projects and can be transferred into a space

You can also import whole spaces and projects into a deployment space. For details, refer to Importing spaces and projects into existing deployment spaces

For information about working with Watson Machine Learning assets, refer to:

Asset types that you can directly add to a space

Connection
Data asset (from a connection or an uploaded file)
Model

For details, refer to these topics:

For data assets and connections: Adding data assets to a deployment space
For models: Importing models into a deployment space

Assets types that are created in projects and can be transferred into a space

Code package
Connection
Data asset (from a connection or an uploaded file)
Data Refinery flow
Data Replication
DataStage Build stage
DataStage Custom stage
DataStage Data definition
DataStage flow
DataStage Function library
DataStage Java Class library
DataStage Match specification
DataStage Operational Decision Manager component
DataStage subflow
DataStage Schema library
DataStage Standardization rule
DataStage Wrapped stage
Environment
Function
Job
Model
Notebook
Parameter set
Pipeline
Script
Shiny App

Note: When you work with a Git-based project, Notebooks, Scripts and Shiny Apps are represented as files within your Git repository. When importing your Git archive file into a deployment space, all of these file types will be accessible within the Code Package asset created as part of the import.

For details, refer to these topics:

Asset types that can be added to a space only as a dependency

Hardware Specification
Package Extension
Software Specification
Watson Machine Learning Experiment
Watson Machine Learning Model Definition

Learn more

Parent topic: Deploying and managing models