Learn how to collaborate on Data Science Projects with DAGsHub. With DAGsHub Storage, sharing data and models becomes as easy as sharing a link, offering collaborators an easy overview of project data, models, code, experiments, and pipelines.
For software engineering teams, tools like Git and remote Git clients like GitHub, GitLab, and BitBucket have made collaboration easy and uncomplicated.
They let different developers in different locations work on and contribute to the same project seamlessly. This ability to easily collaborate on projects has fostered the development of the massive open-source software/libraries ecosystem.
Unfortunately, the same cannot readily be said for data science teams. Even the most adept data science teams still lack best practices for organizing their projects and collaborating effectively.
The data science field is a combination of software engineering and research, that is code + datasets, trained models, and label encodings. Just as it’s elementary to control version history and remotely collaborate on code with a few git commands, data scientists should be able to browse, preview, share, fork, and merge data & models with ease.
Two things have to be in place to aid remote collaboration: version control and remote central storage.
Just as Git allows software engineers to safely go back and forth between different versions of their code, data scientists need to control not only different versions of their code but also different versions of their data.
They should also be able to keep track of what they did to achieve a particular state for a particular version and also be able to reproduce the same state when needed.
So, what are the possible solutions?
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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