Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Check the AWS account and region before deployment
- Identify the active profile when none was specified
- Detect expired credentials or missing configuration
huggingface/skills☆ 1.1만2026년 10월 9일 업데이트
Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Prepare a Python environment for SageMaker deployment
- Separate AWS automation dependencies from system Python
- Check installed AWS package versions
huggingface/skills☆ 1.1만2026년 10월 9일 업데이트
Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Plan AWS hosting for a language model
- Choose a serving route for an embedding model
- Plan asynchronous inference for image generation
huggingface/skills☆ 1.1만2026년 10월 9일 업데이트
Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Check execution roles before deploying a SageMaker endpoint
- Find an existing role when no ARN is provided
- Investigate IAM AccessDenied errors
huggingface/skills☆ 1.1만2026년 10월 9일 업데이트
Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Deploying a real-time model endpoint
- Scaling a sparse workload down to zero instances
- Running long inference jobs asynchronously
huggingface/skills☆ 1.1만2026년 10월 9일 업데이트
Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Choose a container for an LLM deployment
- Select images for embeddings and rerankers
- Check regional image addresses and GPU compatibility
huggingface/skills☆ 1.1만2026년 10월 9일 업데이트