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hf-cloud-sagemaker-production-defaults

Skill

Create a SageMaker endpoint (real-time 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.

0DevOps

Install

git clone https://github.com/huggingface/skills.git ~/.claude/skills/hf-cloud-sagemaker-production-defaults

What is hf-cloud-sagemaker-production-defaults?

Create a SageMaker endpoint (real-time 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 and deploy_async.py for async endpoints (with genuine scale-to-zero support). 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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~155 tokens of context used while enabled, before you invoke anything

Documentation

README · ~11 min read

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
  5. CloudWatch alarms — latency, errors, platform overhead

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