The comparison between technical complexity and debt was first drawn in 1992. In an experience report, Ward Cunningham alerted the industry to the problem and, in doing so, coined the term “Technical Debt.”

Do Machine Learning Models Experience Technical Debt Too?

A recent paper,” Hidden Technical Debt in Machine Learning Systems,” from Sculley et al (2015) suggests so. They explained that machine learning systems induce hidden technical debts in addition to the technical debt that is introduced during software development. There is a crucial difference between hidden and technical debt. Technical debt can be addressed by refactoring code, removing dead code, reducing dependencies, introducing abstractions for easy maintainability, and so on. However, hidden debt is dangerous because it compounds silently.


My blog published as part of SAP Business Objects Analytics Blog Series called Predictive Thursdays has provided a brief summary of the various types of debt that are introduced in machine learning implementations.  If interested in reading further, here is the link : http://blog-sap.com/analytics/2016/08/11/predictive-thursdays-is-your-machine-learning-implementation-debt-free/



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