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Learn AI with Python · Lesson

Scaling and Auto-Scaling Model Endpoints

Kubernetes HPA for model pods, traffic-based scaling, A/B deployment, canary releases.

Why Auto-Scaling

Inference traffic is rarely flat. Provisioning for peak load wastes money at night; provisioning for average load fails at peak. Auto-scaling adjusts the number of replicas automatically based on demand so you pay for what you use and stay responsive.

Pods and Replicas

On Kubernetes a model server runs as a Deployment of identical pods (replicas). Scaling means changing the replica count. A Service load-balances requests across whatever replicas exist.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: model-server
spec:
  replicas: 2
  template:
    spec:
      containers:
        - name: server
          image: my-model:latest

All lessons in this course

  1. Containerizing ML Models with Docker
  2. Cloud Deployment: AWS SageMaker
  3. High-Performance Serving with Triton Inference Server
  4. Scaling and Auto-Scaling Model Endpoints
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