Autoscaling ML Inference Endpoints
Autoscaling matches your ML model's compute to real-time demand, like an elastic container for your inference service. It handles spiky traffic for online endpoints, scaling up for peaks and down to save costs.
Autoscaling for ML inference endpoints automatically adjusts compute resources to match traffic, like an elastic container that expands for peak demand and shrinks to save money. It's crucial for production models with variable request volumes, using rules based on metrics like CPU utilization or fixed schedules. The biggest footgun is misconfiguring the minimum instance count; setting it too low causes significant cold-start latency when the first request arrives after a quiet period, hurting user experience.
Read the original → learn.microsoft.com
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