理解应用行为是扩展 AI 的前提

As AI systems transition from experimental pilots to production-critical enterprise applications, the challenge of reliably scaling them becomes paramount. Traditionally, scaling AI and machine learning workloads has been viewed as a linear process of adding more infrastructure, a method that has proven successful with previous web applications and databases. However, the reality is that scaling AI applications for reliable and sustainable performance does not start with infrastructure. Instead, it begins with understanding application behavior and ensuring that the designed solution can effectively address these behaviors. Only by fully grasping the characteristics and requirements of the application can effective infrastructure scaling be achieved.