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Requirements for using custom components in ML models
You can define your own transformers, estimators, functions, classes, and tensor operations in models that you deploy in IBM Watson Machine Learning as online deployments.
Defining and using custom components
To use custom components in your models, you need to package your custom components in a Python distribution package.
Package requirements
- The package type must be: source distribution (distributions of type Wheel and Egg are not supported)
- The package file format must be:
.zip - Any third-party dependencies for your custom components must be installable by
pipand must be passed to theinstall_requiresargument of thesetupfunction of thesetuptoolslibrary.
Refer to: Creating a source distribution
Supported frameworks
These frameworks support custom components:
- Scikit-learn
- XGBoost
- Tensorflow
- Python Functions
- Python Scripts
- Decision Optimization
For support details, listed by framework, refer to: Supported frameworks
Parent topic: Customizing deployment runtimes