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hf-cloud-sagemaker-production-defaults

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Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

skills/hf-cloud-sagemaker-production-defaults/SKILL.md

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# SageMaker Production Defaults

The difference between a demo endpoint and one you can leave running is: it scales with traffic, it tells you when it breaks, and you can debug it later. This skill makes those three the default rather than optional extras.

By the time this skill runs, the planner has chosen a real-time endpoint, IAM has a usable role, and image-selection has resolved a container URI + AMI version. This skill turns those into an actual deployment.

## What gets created

For every endpoint, the skill creates these as a unit:

1. **SageMaker Model** — image + env vars + execution role + S3 artifacts
2. **Endpoint config** — instance type, initial count, optional data capture
3. **Endpoint** — the real-time endpoint serving inference
4. **Autoscaling target + policy** — target tracking on invocations per instance
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