I wouldn't exactly say that. Jupyter notebooks don't have an easy way to represent an arbitrary DAG. The flow is more linear and narrative like.
That said, we do expect metaflow (with client API) to play very well with notebooks to support a narrative from a business use-case pov; which might be the end-goal of most ML workloads (hopefully).
I would like to think of metaflow, as your workflow construct - hopefully making your life simpler with ML workloads when involving interactions with existing pieces of infrastructure (infra pieces - storage, compute, notebooks or other UI, http service hosting etc.; concepts - collaboration, versioning, archiving, dependency management)
the workflow DAG is a core concept of Metaflow but Metaflow is not just that. Other core parts are managing and inspecting state, cloud integration, and organizing results.
