Arangopipe, a Tool for Machine Learning Meta-Data Management

Tracking #: 696-1676

Responsible editor: 

Brian Davis

Submission Type: 

Resource Paper


Experimenting with different models, documenting results and findings, and repeating these tasks are day-to-day activities for machine learning engineers and data scientists. There is a need to keep control of the machine-learning pipeline and its metadata. This allows users to iterate quickly through experiments and retrieve key findings and observations from historical activity. This is the need that Arangopipe serves. Arangopipe is an open-source tool that provides a data model that captures the essential components of any machine learning life cycle. Arangopipe provides an application programming interface that permits machine-learning engineers to record the details of the salient steps in building their machine learning models. The components of the data model and an overview of the application programming interface is provided. Illustrative examples of basic and advanced machine learning workflows are provided. Arangopipe is not only useful for users involved in developing machine learning models but also useful for users deploying and maintaining them.


Previous Version: 


  • Reviewed

Data repository URLs: 

The data and code associated with this submission are available at:

Date of Submission: 

Tuesday, June 15, 2021

Date of Decision: 

Monday, July 12, 2021

Nanopublication URLs:



Solicited Reviews:

1 Comment

Meta-Review by Editor

I am delighted to inform you that your paper has been accepted for publication! This acceptance is on condition that you address all the remaining minor issues concerning presentation.

Brian Davis (