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 they are performing well and to monitor their accuracy.
To be able to deploy assets from a space, you must have the Watson Machine Learning service installed and provisioned.
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 custom logic:
- Create a Python function to use for creating your online endpoint
- Write a notebook or script to perform batch scoring
Note that if you create a notebook or a script to perform batch scoring such an asset runs as a platform job, not as a batch deployment.
Deployable assets
Here 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 |
Notes:
-
A deployment job is a way of running a batch deployment, or a self-contained asset like a code package or flow in Watson Machine Learning. You can select input and output for your job and choose to run it manually or on a schedule. For details, refer to Creating a deployment job.
-
Notebooks and flows use notebook environments. You can run them in a deployment space, but they are not deployable.
-
You can deploy a Natural Language Processing model by using Python functions or Python scripts. Both online and batch deployments are supported. For more information, refer to Deploying NLP models.
-
You can only use automatic mounts 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. For more information, refer to Known issues and limitations for (Watson Machine Learning).
For more information, refer to:
- Creating online deployments
- Creating batch deployments
- Deploying Python functions
- Deploying scripts
- Deploying NLP models
- Deploying Shiny apps
After you deploy assets, you can manage and update them to make sure they are performing well and to monitor their accuracy. Here are some ways that you can manage or update a deployment:
- Manage deployment jobs. After you create one or more jobs, you can view and manage them from the Jobs tab of your deployment space.
- Update a deployment. For example, you can replace a model with a better-performing version without having to create a new deployment.
- Scale a deployment to increase availability and throughput by creating replicas of the deployment.
- Evaluate deployments in spaces by using Watson OpenScale.
- Delete a deployment to remove a deployment and free up resources.
Learn more
Parent topic: Deploying and managing models