How To Avoid the Technical Debt of Machine Learning

Machine learning provides us an extremely powerful mechanism for building personalized data-driven applications. However, the power doesn’t come without costs or risks. Unfortunately, the downside of long-running machine learning pipelines is all too often hidden from view. Google (NASDAQ: GOOG) recently explored these topics in a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt, which has generated some interesting discussion among users of machine learning. As practitioners we are building tools and models to solve business problems, without thinking too far about maintenance of these models. Those of us who have been running machine learning pipelines in production for a few years have definitely encountered the outlined problems. The Google paper provides some excellent recommendations for building production pipelines without accumulating technical debt. Here are a few…


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