hf-cloud-serving-image-selection

hf-cloud-serving-image-selection is an MLOps & model serving skill for AI agents, published by huggingface in the skills repository on GitHub. Skillspace rates hf-cloud-serving-image-selection C (77/100) and it passed the safety scan; the rating is based on static analysis only.

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

Grade

C

Quality

77 / 100

Safety

Passed

GitHub stars

10,816

Unproven: rated by static analysis only, not yet compared head-to-head.

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