Machine Learning pipelines are at their core a sequence of data transformations. Having a concrete visualization of said transformations may make developing ML pipelines easier, and also allow for clearer communication between developers (and their managers, and potentially future auditors) as to what components they're working on and how they fit together.
Also, by using input output blocks (as opposed to generic functions that can potentially access global state), the data dependencies of different components is made explicit, and can make tooling around that easier. (I don't know how strongly this is enforced in this implementation).
