Prototyping of machine learning (ML) solutions represents a pivotal stage in developing ML-enabled systems. In this course, the prototype serves as a means of communication and should demonstrate the technical feasibility and value to technical and non-technical stakeholders. But, in the context of the current ML solution prototyping process and tooling environment, non-technical stakeholders are limited in their ability to participate effectively, primarily due to the difficulty of understanding the specific ML solution strategy being implemented.

In addition, valuable knowledge is lost during the prototype development process because the process is not sufficiently documented, preserved, or made easily accessible for future projects.

To significantly improve the development of ML prototypes, we propose an extended ML prototyping process and tool support in the form of a toolbox. Preliminary implementations of some tools of the toolbox are presented.