Until Technical Debt In Machine Learning Tear Us Apart

Until Technical Debt In Machine Learning Tear Us Apart

The CACE principle and why technical debt in ML is different. Technical debt is a concern in Software Engineering, but also increasingly in Machine Learning. There are many pitfalls to look for, and principles to follow.

Introduction

Technical debt. If those words have not provoked a shiver down your spine, you might be too novice, or you have entirely given up. In a recent paper¹, a team of Google researchers discuss the technical debt hiding in Machine Learning (ML) Systems.

ML allows us to build useful complex prediction systems quickly, but this does not come for free. The authors remark the technical debt framework can uncover massive ongoing maintenance costs in ML systems such as:

  • Boundary erosion,
  • Entanglement,
  • Hidden feedback loops,
  • Undeclared consumers,
  • Data dependencies,
  • Configuration issues,
  • Changes in the external world,
  • System-level anti-patterns

Developing and deploying ML is nowadays inexpensive, but there is another part to the equation: maintenance over time. This turns out to be difficult and expensive, and it gets worse if technical debt is left unchecked.

software-engineering machine-learning data-science research programming

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