The workloads running in the modern data centers of large scale Internet service providers (such as Amazon, Baidu, Facebook, Google, and Microsoft) support billions of users and span globally distributed infrastructure. Yet, the devices used in modern data centers fail due to a variety of causes, from faulty components to bugs to misconfiguration. Faulty devices make operating large scale data centers challenging because the workloads running in modern data centers consist of interdependent programs distributed across many servers, so failures that are isolated to a single device can still have a widespread effect on a workload. In this dissertation, we measure and model the device failures in a large scale Internet service company, Facebook. We focus on three device types that form the foundation of Internet service data center infrastructure: DRAM for main memory, SSDs for persistent storage, and switches and backbone links for network connectivity. For each of these device types, we analyze long term device failure data broken down by important device attributes and operating conditions, such as age, vendor, and workload. We also build and release statistical models to examine the failure trends for the devices we analyze. Our key conclusion in this dissertation is that we can gain a deep understanding of why devices fail---and how to predict their failure---using measurement and modeling. We hope that the analysis, techniques, and models we present in this dissertation will enable the community to better measure, understand, and prepare for the hardware reliability challenges we face in the future.