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tags:
 - '#machinelearning'
 - '#projects/proposals'
---

# 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.






**References**
- [[(17) (PDF) Machine Learning Pipelines_ Provenance, Reproducibility and FAIR Data Principles--@17PDFMachine]]