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The Machine Learning ecosystem


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Episode Introduction

Sharing what you have developed/learned

Training Data \ # TODO possibly chop if covered elsewhere


Processes, successes and failures beyond sharing the more tangible outcomes of a machine learning project documenting the broader project will help other GLAM institutions apply machine learning. This documentation could include;

There are various ways in which this work can be documented. Academic papers are a possible avenue for sharing the results of experiments but should not be considered as the ‘sole’ medium for sharing meaningful work. The format of many academic journals is likely to preclude sharing ‘failed’ projects and it may be challenging to publish more ‘modest’ uses of machine learning because they are deemed to lack ‘novelty’.

Beyond academic papers, there are a growing number of tools for managing machine learning projects which include data versioning, experiment tracking and other features for documenting work. Public version control repository like GitHub or GitLab offer venus for sharing code and you may explore using other tools like Jupyter notebooks to help make your models more accessible to others.

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Key Points