This chapter examines the critical role of evaluation within the framework of recommender systems, highlighting its significance alongside system construction. We identify three key aspects of evaluation: the impact of metrics on optimization quality, the collaborative nature of evaluation efforts across teams, and the alignment of chosen metrics with organizational goals. Our discussion spans a comprehensive range of evaluation techniques, from offline methods to online experiments. We explore offline evaluation methods and metrics, offline simulation through replay, online A/B testing, and fast online evaluation via interleaving. Ultimately, we propose a multilayer evaluation architecture that integrates these diverse methods to enhance the scientific rigor and efficiency of recommender system assessments.