In the early days of personalization, the focus was on smarter, more complex machine learning models, on algorithms and optimizations. Later, the attention shifted to feature engineering as a driver for accuracy. Finally, the community focused on data as the next frontier: volume, quality, cleansing, and clean labeling. In this talk, we focus on the crucial next step in personalization: well designed software architectures for storing, computing, and delivering responsive, accurate in-product predictions and experiments. To make it concrete, we’ll walk through several Intuit use cases around anomaly detection and personalized in-product experiences.