This document summarizes Bloomberg's use of machine learning for search ranking within their Solr implementation. It discusses how they process 8 million searches per day and need machine learning to automatically tune rankings over time as their index grows to 400 million documents. They use a Learning to Rank approach where features are extracted from queries and documents, training data is collected, and a ranking model is generated to optimize metrics like click-through rates. Their Solr Learning to Rank plugin allows this model to re-rank search results in Solr for improved relevance.