Avoiding Complexity of Machine Learning Problems

Sometimes engineers are prone to overkill- or making machine learning too complex for their needs. This guy from the complexity initiative at Quora gives explanations and valuable tips on how to avoid that. "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. At Quora, we've been using ML to tackle many interesting problems such as ranking, search, recommendation, and spam detection. We are constantly evaluating new approaches and building new product features with ML. At the same time, we also strive to be careful about the complexity that these models introduce and have developed principles and best practices to avoid or reduce such complexity. In this blog post, we will share our thinking about complexity in ML systems and describe some of our approaches to mitigate them."