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 pip and must be passed to the install_requires argument of the setup function of the setuptools library.

Refer to: Creating a source distribution

Supported frameworks

These frameworks support custom components:

  • Scikit-learn
  • XGBoost
  • Tensorflow
  • Python Functions
  • Python Scripts
  • Decision Optimization

For more information, see Supported frameworks

 

Parent topic: Customizing deployment runtimes