This documentation is for Machine Learner 1.0.0. View documentation for the latest release.
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You can extend WSO2 ML using the following extension points.

Input adapters

WSO2 ML has a set of input adapters that are used to read data from different storages such as files, HDFs and registry. You can create an ML Input Adapter by implementing the MLInputAdapter interface, if you need to extend the data reading capability of the ML  as shown below. For instructions on how to create a custom adapter extension, see ML Custom Adapter Extension.

 


Output adapters

WSO2 has a set of output adapters that are used to write data to different storages such as files, HDFS and registry. You can create an ML Output Adapter by implementing the MLOutputAdapter interface, if you need to extend the data writing capability of the ML as shown below. For instructions on how to create a custom adapter extension, see ML Custom Adapter Extension.

Dataset processors

Each data source should have an implementation of DatasetProcessor. Currently, ML supports File, HDFS and DAS as data sources. Therefore we have the following implementation classes.

Model builders

WSO2 ML model generation can be extended by implementing MLModelBuilders. Currently we have a supervised spark model builder and an unsupervised spark model builder. If you need to extend model generation to some other library or a new algorithm type, you can use this extension point of WSO2 ML.

Model Builder class diagram

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