{"id":2464,"date":"2021-08-03T13:40:53","date_gmt":"2021-08-03T21:40:53","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/blog\/comet-industry-qa-collaborative-data-science-and-machine-learning-2\/"},"modified":"2021-08-03T13:40:53","modified_gmt":"2021-08-03T21:40:53","slug":"comet-industry-qa-collaborative-data-science-and-machine-learning-2","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/comet-industry-qa-collaborative-data-science-and-machine-learning-2\/","title":{"rendered":"Comet Industry Q&A (Recap): Collaborative Data Science and Machine Learning"},"content":{"rendered":"\n

A conversation with Abubakar Abid<\/a> of Gradio<\/a> and Jakub Jurovych<\/a> of Deepnote<\/a><\/em><\/h2>\n\n\n\n

As data science and machine learning teams become less siloed, more cross-functional, and increasingly interdisciplinary, the need for sustainable tools and processes for collaboration across the ML model building lifecycle has only increased.<\/p>\n\n\n\n

Given this trend\u2014and the fact that one of Comet’s core values (both internally and with our product stack) is to enable more effective and efficient collaboration\u2014we thought collaboration would be a perfect topic to restart our Industry Q&A series.<\/p>\n\n\n\n

Luckily, we were able to host two industry leaders at the forefront of creating tooling for ML collaboration at different points in the development lifecycle\u2014Abubakar Abid of Gradio, and Jakub Jurovych of Deepnote.<\/p>\n\n\n\n

While there was plenty of awesome conversation and many compelling insights (we’ve collected the best, along with the full session, in this YouTube playlist)<\/a>, I wanted to take a few moments to recap and share a few of my favorite moments<\/p>\n\n\n

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The stages of collaboration in ML\u2014or in any organization<\/h2>\n\n\n\n

To help frame the conversation around collaboration in ML\u2014which admittedly could go in so many different directions, Jakub shared his thoughts about the nature of collaboration more broadly.<\/p>\n\n\n\n

Specifically, he discussed his understanding of collaboration as something that, organizationally speaking, happens both in stages and in relative proportions. I really appreciated his use of general writing as a frame through which to explore collaboration’s basics stages, from creating and tweaking an initial draft in isolation to seeking line edits or deeper peer review.<\/p>\n\n\n\n

The same basic concept holds true for collaboration in ML. Much of the modeling work will still need to be done on the individual level\u2014but to capture value from that 20-30% of the work that requires close and efficient collaboration, teams will need to adopt cross-functional tools and approaches to make sure a collaborative approach is able to scale with you as your team grows.<\/p>\n\n\n\n

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