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FAIR data principles to ML
The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016). These principles provide guidance for scientific data management and stewardship and are relevant to all stakeholders in the current digital ecosystem.
They aim at applying reproducibility, transparency, and reuse of research pipeline.
By focusing on interoperable and reusable principles to ML, it means that it is necessary to have a common technology to describe, find and share the research process and used datasets, thus being able to answer a number of questions including which libraries are used to validate the model, which hyperparameters were used when running the model, how many training runs were performed in the ML pipelines, etc.
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