Continuous Delivery (CD) in AI systems adapts traditional software deployment practices to the complexities of machine learning workflows, focusing on safely releasing new code or models while ensuring every change is validated and deployed with minimal risk. CD pipelines follow deterministic logic triggered by development events like code merges or model updates, minimizing risk and enabling fast, repeatable releases. In contrast, Continuous Training (CT) ensures that machine learning models stay accurate and aligned with evolving real-world data, automating the full loop from performance monitoring to model redeployment. CT operates differently, responding to production signals like data drift or model underperformance, and applies automated validation methods without requiring new ground truth labels. The true power of CD and CT emerges when implemented together as a unified flywheel, transforming static pipelines into dynamic systems that automatically improve over time. This self-reinforcing cycle combines the strengths of both paradigms, enabling organizations to gain a significant competitive advantage by creating scalable, resilient AI infrastructure.