| When you're thinking of "deployment" here - wouldn't it make sense to use the google compute engine for this? I'd be curious to see if there's a legit speed up there with the "real tensorflow". For "on prem" stuff I think "deployment" is going to depend on the actual end use case. Eg:no one in industry will keep their "training data" in git. They'd have an actual database with other systems surrounding it. If it's just "run the model locally to view a web page running in a docker container I wouldn't see the problem here though. The infra will also be different for training vs inference.
For training you'll want gpus, but it's not realistic to run gpus with inference yet. I'd love someone to comment on:
https://developer.nvidia.com/gpu-inference-engine though. There's going to be a lot of non deep learning "stuff" involved here. Much of it will be connected to the use case. Eg: deep learning for log analytics in production will be different than a computer vision pipeline. Warning: highly biased player in the space. |