Engineers and researchers use human computation as a mechanism to produce labeled data sets for product development, research, and experimentation. In a data-driven world, good labels are key. To gather useful results, a successful labeling task relies on many different elements: from clear instructions and user interface design to algorithms for quality control. Furthermore, designing and implementing tasks that produce and use several thousands or millions of labels is different than conducting small scale research investigations. In this talk, I will present a perspective for collecting high quality labels with an emphasis on practical implementations and scalability. I will focus on three main topics: programming crowds, debugging tasks with low agreement, and algorithms for quality control.