Avoiding Complexity of Machine Learning Problems

Today, more and more products and engineering teams rely on machine learning (referred to as ML through out this blog post). The abundance of open source tools and libraries also makes it much easier to learn, develop, and build ML models even for people with little prior knowledge or experience. ML is a powerful tool for many problems, but it comes with costs -- it can introduce complexity to systems which builds up over time and evolves into large technical debt. A recent publication by Google argues that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying ML (see Reference 1). At Quora, we've been using ML to tackle many interesting problems such as ranking, search, recommendation, and spam detection (see Reference 2, 3, and 4).