Technical debt is a metaphor which refers to the implied cost of additional work which happens due to having implemented a quick and simplistic machine learning (ML) solution. Some typical design choices which contribute to technical debt are the project changing requirements, outdated code, insufficient unit and system testing and poor ML documentation. Code refactoring and ML documentation readability can eliminate technical debt in ML systems.
