Customizing Watson Machine Learning deployment runtimes
Create custom Watson Machine Learning deployment runtimes with libraries and packages that are required for your deployments. You can build custom images based on deployment runtime images available in IBM Watson Machine Learning. The images contain preselected open source libraries and selected IBM libraries.
For a list of requirements for creating private Python packages, refer to Requirements for using custom components in ML models.
Cutomizing deployment runtimes for Python
You can customize your deployment runtimes by customizing Python runtimes with third-party libraries and user-created Python packages. If your model requires custom components such as user-defined transformers, estimators, or user-defined tensors, you can create a custom software specification that is derived from a base, or a predefined specification. Python functions and Python scripts also support custom software specifications.
For more information, see customizing Python runtimes with third-party libraries and user-created Python packages.
Parent topic: Deploying and managing assets