Plugins

Plugins are a powerful way to extend the functionality of the ecosystem.Ai platform. You can use plugins to add new features, customize the platform, and integrate with other tools and services. This section provides an overview of the plugin system and the different types of plugins you can create and use.

Why do you need plugins?

Runtime plugins are used to extend scoring functionality by adding custom scoring logic. This allows you to customize the scoring process to meet your specific needs and requirements. Plugins can be used to add new scoring algorithms, preprocessors, postprocessors, and other components to the scoring pipeline.

The following default plugins are available in the ecosystem.Ai platform:

• Pre-predict plugin: This plugin is used to preprocess input data before it is passed to the predictor.
• Post-predict plugin: This plugin is used to postprocess the output of the predictor before it is returned to the client.
• Business Logic plugin: This plugin is used to define custom business logic that can be used to make decisions based on the output of the predictor.
• API plugin: This plugin is used to expose custom endpoints that can be called by external services.
• Algorithm plugin: This plugin is used to define custom scoring algorithms that can be used by the predictor.

Development

You can develop plugins in a number of different ways:

• Workbench: Use the Workbench to develop, test, and deploy plugins in a local environment. You can configure a full build and deployment pipeline interface.
• IntelliJ IDEA: Use IntelliJ IDEA ecosystem.Ai plugin to develop, test, and deploy plugins in a local environment. Use the associated git repo.