The discussion was about pytorch vs tensorflow but your argument seems to be about productionizing research code (which is framework agnostic).
Yes there is a lot of code to be written/optimizations to be done to make things production worthy. However I know a lot of tensorflow research projects that handle data batching in such a terrible way that would take weeks to re-write for production.
As for verboseness of pytorch vs tensorflow, I think either could get more verbose under different circumstances. However for simpler tasks, I think tensorflow is more verbose in general (not accounting for the new release which seems to mimic pytorch/keras a little more). For larger production tasks, its a toss-up depending on whether you need to add new components.
Yes there is a lot of code to be written/optimizations to be done to make things production worthy. However I know a lot of tensorflow research projects that handle data batching in such a terrible way that would take weeks to re-write for production.
As for verboseness of pytorch vs tensorflow, I think either could get more verbose under different circumstances. However for simpler tasks, I think tensorflow is more verbose in general (not accounting for the new release which seems to mimic pytorch/keras a little more). For larger production tasks, its a toss-up depending on whether you need to add new components.