DecentralML tutorial

This tutorial presents an example of integration of machine learning development with DecentralML technology.

This tutorial is based on the roles defined by DecentralML:

• Model creator

• Data annotator

• Model engineer

• Model contributor

and the corresponding task explained in Decentralised Machine Learning documentation. Here's a summary:

• Data annotation

• Model definition and restructuring

• Model training

For each of this task we present a tutorial of all the roles involved and the corresponding functions that need to be executed. These functions are part of the python substrate-client.

All the tasks involve the model creator for creating the task, the required files and assets, and for validating the results.

This tutorial relies on separated scripts and files as assets to complete the machine learning tasks created by the model creator. The structure of the assets will be indicated for each machine learning task.