Arangopipe, a Tool for Machine Learning Meta-Data Management

Tracking #: 690-1670

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 lifecycle. 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 are 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.



  • Reviewed

Data repository URLs: 

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

Date of Submission: 

Friday, March 26, 2021

Date of Decision: 

Monday, June 7, 2021

Nanopublication URLs:



Solicited Reviews:

1 Comment

Meta-Review by Editor

Your paper is quite interesting and could be valuable contribution for ML engineers but the manuscript needs to significanty reviewed.   If you intend to submit a revision please ensure to address each reviewer's comments in point by point fashion.


  • In interesting contrbution which would be of value to to ML engineers.


  • Weak (non peer reviewed) referenceing  (See Review 2)
  • Poor editing in that the paper reads more like project description than a scientific article (See Review 2,3)
  • Insufficient technical description in places (See Reviewer 3 for section by section details.


Recommendation 1:  Address the technical gaps in the manuscript.

Although well motivtated the manscript is lacking in techincal details and examples/illustrations in various sections.  These gaps must be addressed in a future revision.  See Review 2 for minor comments and Review 3 comments in particular for further corrections.

Recommendation 2:  Improve the scientific writing and referencing significantly.
-Clean up the referencing and engage with proper peer reviewed citations in order to correctly support claims in the manuscript.
-Adapt the content to the apprioriate style for a scientific article.
-See Review 2 comments in particular for further corrections.

Brian Davis (