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Stop guessing auto scaling targets for TTS on Amazon SageMaker AI

AWS Machine Learning Joseph Cottingham

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Source summary (English)

Teams deploying custom text-to-speech models on Amazon SageMaker AI often guess at autoscaling thresholds, and a wrong guess either wastes GPU spend or breaches a latency SLO. This post deploys Kokoro-82M as a custom container and derives a measured autoscaling policy from two live-endpoint measurements, Q_max and T_total, instead of guesswork.

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