Google Drive is a cloud storage and collaboration service used by hundreds of millions of users around the world. Quick Access is a new feature in Google Drive that surfaces the relevant documents to the user on the home page. We describe the development of a machine-learned service behind this feature. Our metrics show that this feature cuts the time it takes for users to locate their documents in half. The development of this product feature is an illustration of a number of more general challenges and constraints associated with machine learning product deployment such as dealing with private corpora and protecting user privacy, working with data services that are not designed with machine-learning in mind and may be owned and operated by different teams with different constraints, and evolving product definitions which inform the metric being optimized. We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine-learning problems.