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SageMaker Deployment Planner

Plans how to deploy a model to Amazon SageMaker, picking the serving pathway and handing off to other deployment skills.

Source Download skill
Repository
huggingface/skills
Stars
11,253
Licence
Apache-2.0
Files
1 file

What it does

When you ask an AI agent to put a model on AWS, this skill makes it act as the planner for that job. It asks a few short questions (which model, roughly how much traffic, how fast responses must be), recommends one of several hosting options such as a real-time endpoint, scale-to-zero, or async inference, and checks whether your AWS account has enough GPU quota. It then hands the actual setup to companion skills for credentials, environment, image selection and deployment.

Great for

  • Developers who want to host a Hugging Face or fine-tuned model on SageMaker without knowing which endpoint type to pick
  • Teams deciding between always-on real-time endpoints and cheaper scale-to-zero or async setups
  • Users who only know they want a model 'running on AWS' and need the agent to choose defaults

Skip it if

  • People without an AWS account or without permission to create SageMaker resources, since the workflow depends on them
  • Interactive apps that cannot tolerate the slow first request after an idle scale-to-zero endpoint (every request during wake can fail with a 400)
  • Serverless, batch transform or Bedrock import deployments, which this skill says are not scripted and only get a hand-off explanation

Try asking

  • “Deploy this Hugging Face LLM on AWS for about 100 requests a day, and tell me which endpoint type to use”
  • “Host my fine-tuned embedding model on SageMaker for low-cost, mostly CPU inference”
  • “Set up an async endpoint for my text-to-image diffusion model because each image takes over a minute”
  • “I just want this reranker running on AWS, you figure out the rest”
Step-by-step guide: how to use SageMaker Deployment PlannerInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 1 original files, the Apache-2.0 licence and an attribution note.

    hf-cloud-sagemaker-deployment-planner.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip hf-cloud-sagemaker-deployment-planner.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip hf-cloud-sagemaker-deployment-planner.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the model name and a rough requests-per-day figure up front so the agent asks as little as possible
  • Check GPU quota early: default endpoint quotas for GPU instances are often 0, and a quota increase can take hours to days
  • Review any AWS resources the agent creates, since deployments cost money and idle always-on endpoints keep billing

What's inside

  • SKILL.md

Redistributed unmodified from huggingface/skills under the Apache-2.0 licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.