When every app or website has a search box, it is crucial to level up your search engine to rise past the competition and increase your bottom line. Introducing machine learning to your search engine is complicated but worth it – so let’s break it down. This talk details the transition from a simple weight-based search engine to a machine learning powered, data driven setup; and how to achieve the „Always be testing“ state with rapid iteration cycles. It covers an end-to-end search engine architecture, from data logging from Microservices, processing with Apache Spark, training with LambdaMART, and deploying your models with ONNX on top of ElasticSearch or Solr.