I think surfacing the data is just the first step, often times data scientists need to run some data exploration, the process is generally iterative, and so they need to run several experiments, resume or restart some experiments, scale training with distributed learning using several machines, or run hyper-parameters tuning, which means handling failures, visualize and debug results, before deciding if they should deploy a model. Once a model is deployed the story does not end there, because models become stale and need to be retrained. There are other issues related to compliance that need to be handled as well, and many other problems related to governance, a/b testing, ...
The good news is that there are several open source initiatives to solve several of these problems, at Polyaxon [0] we are trying to solve some of the aspects related to the experimentation phase.