With model failures leading to some high-profile financial accidents in the past few years, there has been a renewed emphasis to systematically address model risk in the last few years. Regulatory agencies have issued guidance documents and various organizations including QuantUniversity are offering model review and validation services to assist organizations to comprehensively manage model risk for their quantitative models. With the financial industry heavily relying on quantitative models and with serious repercussions of model failures, it has become essential that organizations prioritize model risk management. Companies are recognizing the need to develop robust processes to address various aspects of risk evolving from the use and deployment of their quantitative models. Though, a lot has been written about model risk management, few discuss practical tools quants could use to measure and assess model risk as a part of the quant development process. In many of the consulting projects we have worked on, model risk management is typically an afterthought usually delegated to the risk organization after quants have completed their development work. This “handoff” increases the possibility of model failures and the consequences could be too acute to remedy. At QuantUniversity, we believe that measurement and assessment of model risk should be an integral part of the quant development process. In this article, we outline some of the tools quants could use to integrate measurement and assessment of model risk as a part of their development process. We start out by orienting quants on model risk and discuss drivers that have brought model risk into the limelight. We then discuss challenges quants have in integrating model risk into the quant development process and introduce practical tools quants can use to quantify model risk, build control measures to manage and prioritize these risks.