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Inference observability: latency percentiles, token metrics, and drift at the endpoint
An inference endpoint can be healthy by every conventional infrastructure metric and still deliver a terrible user experience.
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GPU right-sizing and autoscaling: serving models without burning money
A GPU serving a machine learning model can be expensive even when it is doing almost nothing.
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Dynamic batching: the single biggest throughput lever in model serving
A model server processing one request at a time pays fixed costs for every execution:
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From pickle to production: wrapping a model in a real API that won’t fall over
Training a machine learning model is only half the job. The other half is making it available reliably, safely, and predictably.