Many software services are nowadays hosted on cloud computing platforms, like Amazon EC2, due to many benefits like reduced operational costs. However, node failures in these platforms can impact the availability of their hosted services and potentially lead to large financial losses. Predicting node failures before they actually occur is crucial as it enables DevOps engineers to minimize their impact by performing preventative actions. However, such predictions are hard due to many challenges like the enormous size of the monitoring data and the complexity of the failure symptoms. AIOps, a recently introduced approach in DevOps, leverages data analytics and machine learning (ML) to improve the quality of computing platforms in a cost-effective manner. However, the successful adoption of such AIOps solutions requires much more than a top-performing ML model. Instead, AIOps solutions must be trustable, interpretable, maintainable, scalable, and evaluated in context. To cope with these challenges, in this paper we report our process of building an AIOps solution for predicting node failures for an ultra-large-scale cloud computing platform at Alibaba. We expect our experiences to be of value to researchers and practitioners, who are interested in building and maintaining AIOps solutions for large-scale cloud computing platforms.