Managing predictive deployments
For proper deployment, you must set up a deployment space and then select and configure a specific deployment type. After you deploy assets, you can manage and update them to make sure that they perform well and to monitor their accuracy.
To be able to deploy assets from a space, you must have a machine learning service instance that is provisioned and associated with that space.
Online and batch deployments provide simple ways to create an online scoring endpoint or do batch scoring with your models.
If you want to implement a custom logic:
- Create a Python function to use for creating your online endpoint
- Write a notebook or script for batch scoring
Deployable assets
Following is the list of assets that you can deploy from a watsonx.ai Runtime space, with information on applicable deployment types:
| Asset type | Batch deployment | Online deployment |
|---|---|---|
| Functions | Yes | Yes |
| Models | Yes | Yes |
| Scripts | Yes | No |
- A deployment job is a way of running a batch deployment, or a self-contained asset like a flow in watsonx.ai Runtime. You can select the input and output for your job and choose to run it manually or on a schedule.
- Notebooks and flows use notebook environments. You can run them in a deployment space, but they are not deployable.
After you deploy assets, you can manage and update them to make sure that they perform well and to monitor their accuracy. When you don't need your deployment anymore, delete it.