hf-cloud-sagemaker-deployment-planner
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.
Target: Cloud, operations & security. Automatically collected · unverified.
Excerpt from the author’s documentation
Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.
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Topics in the source
- SageMaker Deployment Planner
- Workflow phases
- Discovery: ask only what you need
- Pathway selection
- Instance selection: check quota before recommending
- Style
You are helping a user deploy a model to Amazon SageMaker. Most users invoking this skill want the model deployed with reasonable defaults, in as few questions as possible. Ask only what you need, recommend a pathway honestly, and hand off to the specialized skills. 1. Discovery — what is being deployed and what are the constraints (this skill) 2. Pathway selection — real-time / serverless / async / batch / Bedrock CMI (this skill) 3. Context preflight — hf-cloud-aws-context-discovery, then hf-cloud-python-env-setup 4. IAM preflight — hf-cloud-sagemaker-iam-preflight 5. Image selection — hf-cloud-serving-image-selection 6. Deployment — hf-cloud-sagemaker-production-defaults Phases 1–2 are this skill's job. The others activate when their patterns match. You will eventually need to know: What model: HuggingFace ID, S3 path to artifacts, or model name. If the user is vague ("the model I fine-tuned"), ask for the artifact location. Model type: text-generation LLM, embedding/reranker, or other (classifier, NER, etc.). This determines the serving stack — usually inferable from the model name (anything ending in -embed-, starting with BAAI/bge-, sentence-transformers/ etc. is embeddings;
This is an excerpt. Refer to the complete source and its current requirements.
- Source repository
- huggingface/skills
- Source file
- skills/hf-cloud-sagemaker-deployment-planner/SKILL.md
- Fixed revision
- 25910b3d0ba31a2ba102f901cdc9e008c278a69c
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This record was collected automatically. Its documentation, suitability and runtime behavior have not been manually verified.
Author: huggingface/skills · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T16:01:02.684Z.
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