It is humbling to think of the number of tools, languages, techniques and applications a machine learning ecosystem has nurtured. Choosing the best fit out of these hundreds of options and then bringing them together to work seamlessly is a data scientist’s nightmare. The hidden technical debts in a machine learning (ML) pipeline can incur massive maintenance costs.
According to a report presented by the researchers at Google, there are several ML-specific risk factors to account for in system design:
Boundary erosion
Entanglement
Hidden feedback loops
Undeclared consumers
Data dependencies
Configuration issues
Technical debt, popularised by Ward Cunningham in 1992 with a metaphor, represents the long term costs incurred by moving quickly in software engineeri
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Ram Sagar
I have a master's degree in Robotics and I write about machine learning advancements.
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