Modern relevance in search engines has come a long way since the early days of information retrieval, when the likes of TF-IDF and BM25 scoring models first came on the scene. And while those core models are still good for a first pass retrieval, more and more search engines are employing machine learning, natural language processing and sophisticated re-ranking techniques to fine tune relevance. This talk will provide a review of current best practices in relevance tuning, including what to measure and how to measure it. We’ll then give details on how to use techniques like learning to rank and query intent classification to improve results, with examples in Apache Solr. We’ll finish with a sneak peak into using deep learning and word2vec in a search context.