hf-cloud-sagemaker-deployment-planner

原文涵盖的主题

• 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;

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