In this talk we will discuss a ensemble technique for machine learning known as stacking and how it can be used for predictive modeling. The use case we will focus on is to predict an internet user's likelihood of responding to online advertising. We will describe ensemble learning in general and then stacking more specifically. We will introduce techniques to improve the predictive power of a stacked model. Finally, we will discuss how we deploy our stacked models in production at scale.