> most people haven’t figured out that ML oriented processes almost never scale like a simpler application would. You will be confronted with the same problem as using SAP; there is a ton of work done up front; all of it custom. I’ll go out on a limb and assert that most of the up front data pipelining and organizational changes which allow for [ML to be used operationally by an org] are probably more valuable than the actual machine learning piece.
Strong agreement from me: I've never worked on deploying ML models, but have worked on deploying operations-research type automated decision systems that have somewhat similar data requirements. Most of the work is client org specific in terms of setting up the human & machine processes to define a data pipeline to provide input and consume output of the clever little black box. A lot of this is super idiosyncratic & non repeatable between different client deployments.
Strong agreement from me: I've never worked on deploying ML models, but have worked on deploying operations-research type automated decision systems that have somewhat similar data requirements. Most of the work is client org specific in terms of setting up the human & machine processes to define a data pipeline to provide input and consume output of the clever little black box. A lot of this is super idiosyncratic & non repeatable between different client deployments.