Dan Shiebler

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]

Introduction Many prediction problems can be framed as “given the knowledge that this sample belongs to categories \(A,B,C,\cdots,D\), predict something about this sample.” As a concrete example, suppose we would like to use linear regression to predict the value of a transaction based on a small set of categorical features... [Read More]

This post is part three in a three part series on the challenges of improving a production machine learning system. Find part one here and part two here. A/B Testing When engineers and data scientists optimize machine learning systems they often focus on improving offline metrics like cross entropy, ROC-AUC,... [Read More]

This post is part two in a three part series on the challenges of improving a production machine learning system. Find part one here and part three here. Adding New Features or Improving Existing Features A machine learning model is only as powerful as the features it is trained with.... [Read More]