Dan Shiebler

ML models are collaboration bottlenecks. Suppose your team owns a binary classification model, and you have decided that increasing this model’s recall without reducing precision is very important for the business. You’ve put 5-10 engineers on the job. The Problem What should these engineers do? There are many ways to... [Read More]

One of the hallmarks of a great engineer is the ability to execute. An engineer who can execute gets things done. They crush tickets, pump out designs, spin up features, answer critical data questions, improve models, and prototype new ideas quicker than their peers. How to Execute Engineering projects are... [Read More]

A common problem in machine learning is “use this function defined over this small set to generate predictions over that larger set.” Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In a recent... [Read More]

A boxer who only punches a bag will fail in the ring, and an ML model that only learns with clean data will fail in production. We need to let our model get punched in the face in training if we want it to perform well when distributions drift. The... [Read More]

Many real world problems can be framed as regression: use a collection of features \(X\) to predict a real-valued quantity \(y\). However, this framing can obfuscate a very important detail: which kinds of mistakes are most important to avoid? Many factors can influence this, including how the predictions will be... [Read More]