Open Source Summit + ELC North America 2019

The rise of new AI and ML requires new workflows and new tools: data versioning, ML pipeline versioning, experiments metrics visualization and others that have not been formalized and even named yet.

The traditional software engineering toolset does not fully cover ML team's needs. We will discuss the current practices of organizing ML workflow using traditional open-source tools like Git and Git-LFS as well as their limitations. Thereby motivation for developing new ML specific experiments and data management systems will be explained.

ML workflow differs from software engineering. Experimentation, trials-and-errors nature of ML projects and the need in more granular and efficient data artifacts management requires new sets of development tools. We will show ideas behind open source tool DVC or http://dvc.org which focuses on working with ML experiments, managing large datasets, and ML model.