disclaimer I am one of the authors of an open-source solution (https://github.com/polyaxon/polyaxon) that specializes in the experimentation and automation phase of the data-science lifecycle.
Our tool provides exactly the kind of abstraction you mentioned:
* Training, data operations, and interactive workspaces (https://polyaxon.com/docs/experimentation/)
* A scalable history and comparison table (https://polyaxon.com/docs/management/runs-dashboard/comparis...)
* Currently pipelines and concurrency management is on the commercial version (https://polyaxon.com/docs/automation/) but several companies use Polyaxon with other tools like Kubeflow (https://medium.com/mercari-engineering/continuous-delivery-a...) or it can be used with MetaFlow for the pipelines part.
I would really like to hear your thoughts and feedback.