slug: amazon-sagemaker provider: Amazon SageMaker generated_by: planning/capability-mapping/scripts/classify_capabilities.py model: claude-opus-5 frame: - Education min_confidence: 0.7 capability_model: source: https://github.com/vincentmakes/turbo-ea-capabilities license: CC-BY-4.0 attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities, CC BY 4.0 notice: NOTICE edge_count: 4 edges: - tag: Models spec_file: amazon-sagemaker-models-api-openapi.yml capability_id: BC-610.60 capability_id_l1: BC-610 capability_name: Artificial Intelligence Management confidence: 0.9 evidence: POST /#CreateModel Amazon SageMaker Create a Model reason: Creating and describing machine-learning models is squarely AI/ML model lifecycle management. - tag: Training Jobs spec_file: amazon-sagemaker-training-jobs-api-openapi.yml capability_id: BC-610.60 capability_id_l1: BC-610 capability_name: Artificial Intelligence Management confidence: 0.9 evidence: POST /#CreateTrainingJob Amazon SageMaker Create a Training Job reason: Model training job orchestration is a core part of the AI/ML model lifecycle (MLOps). - tag: Endpoints spec_file: amazon-sagemaker-endpoints-api-openapi.yml capability_id: BC-610.60 capability_id_l1: BC-610 capability_name: Artificial Intelligence Management confidence: 0.8 evidence: POST /#CreateEndpoint Amazon SageMaker Create an Endpoint reason: Deployment of ML model inference endpoints on a machine-learning platform — AI/ML model lifecycle and MLOps. - tag: Notebook Instances spec_file: amazon-sagemaker-notebook-instances-api-openapi.yml capability_id: BC-610.60 capability_id_l1: BC-610 capability_name: Artificial Intelligence Management confidence: 0.7 evidence: POST /#CreateNotebookInstance Amazon SageMaker Create a Notebook Instance reason: Managed Jupyter notebook instances for data science work on an ML platform; supports AI/ML lifecycle, though it is also infrastructure provisioning.