I manage a ML team and the article has several ‘borrowable’ ideas, the primary idea supporting model builders working on their local Linux device and simplifying the process of transferring locally built models to a scalable platform. If we were starting today to implement our in house system I might just use Ubber’s system as a checklist for features to implement.
Not too off topic: after mostly building and training models on remote compute (both at work and for my at home learning projects), I recently bought a fast laptop for home use that has a 1070 GPU and the experience of using a local device with a GPU makes me regret many years of SSHing to remote compute or using Jupyter remotely for machine learning development. I mention this as a lesson learned that may be helpful.
Not too off topic: after mostly building and training models on remote compute (both at work and for my at home learning projects), I recently bought a fast laptop for home use that has a 1070 GPU and the experience of using a local device with a GPU makes me regret many years of SSHing to remote compute or using Jupyter remotely for machine learning development. I mention this as a lesson learned that may be helpful.