Amazon SageMaker · For data scientists

Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.

huggingface/skills

Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR).

awslabs/agent-plugins

Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes.

awslabs/agent-plugins

Entry point for hosting a model on Amazon SageMaker: asks a few questions, picks a deployment pathway and hands off to the specialist skills.

huggingface/skills

Deep expertise in ML/CV model selection, training pipelines, and inference architecture.

alirezarezvani/claude-cto-team

Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables.

aws/agent-toolkit-for-aws