Wenjie Zi highlighted a critical challenge at QCon SF 2024, revealing that 85% of machine learning deployments fail after leaving the lab due to "silent failures," where models drift away from reality without triggering traditional software monitoring alerts. These failures occur because machine learning systems, unlike conventional software, require continuous monitoring of inputs, predictions, and outcomes to detect deviations that could lead to significant business impacts. The article emphasizes that model monitoring, which tracks metrics like prediction drift and feature distribution, should be complemented by model observability to provide context and diagnose failures. It discusses the inadequacy of existing monitoring tools for addressing statistical decay and the necessity for advanced strategies like real-time anomaly detection, automated compliance checks, intelligent alerting, optimized infrastructure, and predictive monitoring to prevent failures and maintain trust. Galileo's platform is presented as a solution, offering cost-effective evaluation models and comprehensive monitoring capabilities that support enterprise-scale deployments and compliance requirements, ultimately ensuring that machine learning systems remain effective and aligned with business objectives.