How to Measure Autoscaling Targets for SageMaker TTS
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According to the AWS Machine Learning Blog, on October 23, 2026 AWS published a tutorial on how to use measured data instead of guesswork to set autoscaling targets for TTS services on Amazon SageMaker AI. The tutorial deploys the Kokoro-82M TTS model in a custom container on a SageMaker AI real-time endpoint, and obtains two key figures through measurement: Q_max, the maximum concurrency a single instance can handle without breaching the SLO, measured at 50; and T_total, the total time of a full scaling cycle, measured at 260.2 seconds, which together with a 60-second detection lag from throttling adds up to 320.2 seconds. The blog uses this to show that a measurement-based approach to setting scaling targets avoids the bias of experience-based guesswork. This event currently consists of a single tutorial published on the official AWS blog, with no follow-up coverage or responses from involved parties.
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- AWS Machine Learning BlogStop guessing auto scaling targets for TTS on Amazon SageMaker AI
The AWS machine learning blog published a tutorial that deploys the Kokoro-82M TTS model in a custom container on an Amazon SageMaker AI real-time endpoint, replacing guessed auto scaling targets with two measured values: Q_max, the maximum concurrency a single instance can handle without breaching the SLO (measured at 50), and T_total, the full duration of one scaling cycle (measured at 260.2 seconds, or 320.2 seconds including a 60-second upper bound on detection lag).
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