Deploying foundation model assets
Deploy foundation model assets to test the assets, put them into production, and monitor them.
Service The watsonx.ai service and other supplemental services are not available by default. An administrator must install these services on the IBM Cloud Pak for Data platform. To determine whether a service is installed, open the Services catalog and check whether the service is enabled.
Deploying a tuned model asset
After you tune a foundation model and save the tuned model as a project asset, you can promote it to a deployments space. From the space, you can test the tuned model and get the endpoint for putting the asset to productive use.
For details, see Deploying a tuned foundation model.
Deploying a prompt template asset
After you save a prompt template as a project asset, you can promote it to a deployment space. From the space, you can test the prompt template and get the endpoint for putting the asset to productive use.
If you have the watsonx.governance service, you can also capture and track the deployment details for a prompt template to meet governance requirements.
For details, see Deploying a prompt template.
Bring your own foundation model to inference from watsonx.ai
In addition to working with foundation models that are curated by IBM, you can upload and deploy your own foundation models. After the models are deployed and registered with watsonx.ai, create prompts that inference the custom models from the Prompt Lab.
Deploying a custom foundation model provides the flexibility for you to implement the AI solutions that are right for your use case.
For more information, see Deploying a custom foundation model.
Learn more
- Tracking prompt templates
- Evaluating a prompt template in a deployment space
- Tuning a foundation model.
Parent topic: Deploying assets with Watson Machine Learning