Orchestrating CNN Training and Inference Workflows With Temporal

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.