Deploying a real-time inference endpoint
WHAT IT TESTS: model serving operations. OUTLINE: package the model artifact and inference code in a container, choose instance type and autoscaling, configure the endpoint with health checks, and plan safe rollout like canary plus monitoring.
WHAT IT TESTS: whether you can operationalize a model as a managed serving endpoint, not just train it. ANSWER OUTLINE: package the model artifact plus preprocessing and inference code into a serving container, register it, and create an online endpoint; configure compute type (CPU or GPU), instance count and autoscaling on traffic, request and response handling, health and readiness checks, and authentication; plan a safe rollout with canary or blue-green traffic splitting and attach monitoring and logging for latency, errors, and data drift.
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- #cloud
- #machine-learning
- #model-serving
- #inference
- #mlops
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