Search is the first line of defense when it comes to a customer looking for what to buy on an e-commerce website. If a customer can’t find what they are looking for you are likely to lose the customer or worse never acquire them. In addition to being critical to the E-Commerce experience, providing personalized search results pose a challenging and intellectually stimulating engineering problem. In this session, we will use Search as a case study to focus on the technology that allows us to serve these models in highly scalable production environments. We will go over the platforms that power Zappos’ personalized search engine, focusing on model retraining strategies, automated model deployments, and the microservice APIs that expose these powerful machine learning algorithms to the customers, while meeting Tier 1 Latency SLAs.