hf-cloud-sagemaker-production-defaults Skill

Source

• Creator: huggingface
• Repository: huggingface/skills
• Skill file: skills/hf-cloud-sagemaker-production-defaults/SKILL.md

What it does

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 createendpoint, or finalize a deployment after the image URI and IAM role are kno

Skill instructions

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 An inference-component deployment (deployic.py) creates the same set with two changes: the endpoint config carries the execution