Guessing autoscaling targets for custom text-to-speech models on Amazon SageMaker AI is a recipe for wasted GPU spend or breached latency SLOs. By deploying Kokoro-82M as a custom container, you can derive a measured autoscaling policy from two live-endpoint measurements, Q_max and T_total, replacing arbitrary thresholds with production telemetry. #AWS #MachineLearning #SageMaker #DevOps
Guessing autoscaling targets for custom text-to-speech models on Amazon SageMaker AI is a recipe for wasted GPU spend or...
