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 install and provision the Watson Machine Learning service.
Service
The Watson Machine Learning service is not available by default. An administrator must install this service on the IBM Cloud Pak for Data platform. To determine whether the
service is installed, open the Services catalog and check whether the Watson Machine Learning service is enabled.
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 Watson Machine Learning space, with information on applicable deployment types:
| Asset type | Batch deployment | Online deployment | App deployment |
|---|---|---|---|
| Functions | Yes | Yes | No |
| Models | Yes | Yes | No |
| Scripts | Yes | No | No |
| Shiny apps | No | No | Yes |
An R Shiny app is the only asset type that is supported for web app deployments.
- A deployment job is a way of running a batch deployment, or a self-contained asset like a flow in Watson Machine Learning. 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.
- If you save an AutoAI experiment as a notebook in your project and then promote the notebook from your deployment space, your notebook job might fail. This can happen if the runtime environment that is selected to run the deployment job for the notebook contains fewer resources than the runtime environment that was originally used to run the AutoAI experiment. To avoid failure, your must promote the notebook and the environment separately to your deployment space.
- You can use automatic mounts only for storage volumes with Watson Machine Learning Shiny app deployments and notebook runtimes. You cannot use automatic mounts for storage volumes with online and batch deployments because they are not supported by Watson Machine Learning.
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.