MLOSS 2018: Sustainable communities

open source

Slides and resources from my invited presentation at the Machine Learning Open Source Software (MLOSS) workshop on sustainable communities at NeurIPS 2018.


Mara Averick


Dec 8, 2018

About the workshop

As described in the MLOSS 2018 CfP:

Machine learning open source software (MLOSS) is one of the cornerstones of open science and reproducible research. Once a niche area for ML research, MLOSS today has gathered significant momentum, fostered both by scientific community, and more recently by corporate organizations. The past workshops, from NIPS’06 to ICML’15, successfully brought together researchers and developers from both fields, to exchange experiences and lessons learnt, to encourage interoperability between people and projects, and to demonstrate software to users in the ML community.

Continuing the tradition in 2018, this year’s workshop that is a mix of invited speakers (NumFOCUS, tidyverse, openML, GPFlow, Eigen3), contributed talks/demos, and discussion/activity sessions. This year’s headline aims to give an insight of the challenges faced by projects as they seek long-term sustainability, with a particular focus on community building and preservation, and diverse teams.

Indeed, the workshop, talks, and discussion, included speakers and participants focused on an array of programming languages from different scientific communities, and was an amazing peer-to-peer exchange of techniques and lessons learned for driving community engagement and sustainability.


Slides from my presentation (embedded, below), can also be found with working links in their GitHub repo, as well as selected resources from both the talk and the panel discussion that followed.

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BibTeX citation:
  author = {Averick, Mara},
  title = {MLOSS 2018: {Sustainable} Communities},
  date = {2018-12-08},
  url = {},
  langid = {en-US}
For attribution, please cite this work as:
Averick, Mara. 2018. “MLOSS 2018: Sustainable communities.” December 8, 2018.