Convolutional neural network workloads rarely fail because the forward pass is mathematically difficult. They fail because modern training and inference pipelines are distributed systems: datasets arrive late, GPU workers disappear, validation jobs stall, model registration breaks halfway through, and long-running executions need to resume without corrupting state. Temporal is designed for exactly that class of problem. A Temporal Workflow Execution is durable, reliable, and scalable, and Temporal defines durable execution as the ability of a workflow to maintain state and progress through crashes or outages. That makes it a strong fit for CNN pipelines whose control plane must survive for hours, days, or even longer while the actual tensor computation runs elsewhere.