name: Amazon SageMaker description: Amazon SageMaker is a fully managed machine learning platform that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker removes the heavy lifting from each step of the machine learning process, providing built-in algorithms, managed Jupyter notebooks, distributed training, automatic model tuning, and one-click deployment to production endpoints with auto-scaling. url: https://aws.amazon.com/sagemaker/ baseURL: https://api.sagemaker.amazonaws.com kind: company created: '2024-01-01' modified: '2026-05-19' tags: - AI - AWS - Inference - Machine Learning - MLOps - Training apis: - name: Amazon SageMaker API description: The Amazon SageMaker control plane API for creating and managing SageMaker resources including notebook instances, training jobs, models, endpoints, pipelines, experiments, feature groups, and monitoring schedules. humanURL: https://docs.aws.amazon.com/sagemaker/latest/APIReference/Welcome.html baseURL: https://api.sagemaker.{region}.amazonaws.com tags: - Machine Learning - AI - Training - Inference properties: - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/Welcome.html - type: OpenAPI url: openapi/amazon-sagemaker-openapi.yml - type: JSONSchema url: json-schema/amazon-sagemaker-notebook-instance-schema.json - type: JSONSchema url: json-schema/amazon-sagemaker-training-job-schema.json - type: JSONSchema url: json-schema/amazon-sagemaker-model-schema.json - type: JSONSchema url: json-schema/amazon-sagemaker-endpoint-schema.json - type: SDK url: https://pypi.org/project/sagemaker/ title: Python SDK - type: CodeExamples url: https://github.com/aws/amazon-sagemaker-examples title: Jupyter Notebook Examples - type: NaftikoCapability url: capabilities/amazon-sagemaker-endpoints.yaml - type: NaftikoCapability url: capabilities/amazon-sagemaker-models.yaml - type: NaftikoCapability url: capabilities/amazon-sagemaker-notebook-instances.yaml - type: NaftikoCapability url: capabilities/amazon-sagemaker-training-jobs.yaml - name: Amazon SageMaker Runtime API description: The Amazon SageMaker AI runtime API for invoking deployed model endpoints to get real-time inference predictions. humanURL: https://docs.aws.amazon.com/sagemaker/latest/dg/API_runtime_InvokeEndpoint.html baseURL: https://runtime.sagemaker.{region}.amazonaws.com tags: - Inference - Runtime - Machine Learning properties: - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/dg/API_runtime_InvokeEndpoint.html - name: Amazon SageMaker Feature Store Runtime API description: Data plane API operations for the Amazon SageMaker Feature Store supporting put, delete, and retrieve operations for ML features. humanURL: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_SageMaker_Feature_Store_Runtime.html baseURL: https://featurestore-runtime.sagemaker.{region}.amazonaws.com tags: - Feature Store - Machine Learning - Data properties: - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_SageMaker_Feature_Store_Runtime.html - name: Amazon SageMaker Metrics Service API description: Data plane API operations for Amazon SageMaker Metrics for putting and retrieving metrics related to training runs. humanURL: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_SageMaker_Metrics_Service.html baseURL: https://metrics.sagemaker.{region}.amazonaws.com tags: - Metrics - Training - Machine Learning properties: - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_SageMaker_Metrics_Service.html - name: Amazon SageMaker Geospatial API description: APIs for creating and managing Amazon SageMaker geospatial capabilities including earth observation jobs and vector enrichment jobs. humanURL: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_SageMaker_geospatial_capabilities.html baseURL: https://sagemaker-geospatial.{region}.amazonaws.com tags: - Geospatial - Machine Learning - AWS properties: - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_SageMaker_geospatial_capabilities.html - name: Amazon SageMaker Edge Manager API description: SageMaker Edge Manager dataplane service for communicating with active edge agents running ML models on edge devices. humanURL: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_Sagemaker_Edge.html baseURL: https://edge.sagemaker.{region}.amazonaws.com tags: - Edge - IoT - Machine Learning properties: - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Operations_Amazon_Sagemaker_Edge.html common: - type: Portal url: https://aws.amazon.com/ - type: GettingStarted url: https://aws.amazon.com/sagemaker/getting-started/ - type: Documentation url: https://docs.aws.amazon.com/sagemaker/latest/dg/ - type: APIReference url: https://docs.aws.amazon.com/sagemaker/latest/APIReference/ - type: Console url: https://console.aws.amazon.com/sagemaker/ - type: SignUp url: https://portal.aws.amazon.com/billing/signup - type: Pricing url: https://aws.amazon.com/sagemaker/pricing/ - type: FAQ url: https://aws.amazon.com/sagemaker/faqs/ - type: Blog url: https://aws.amazon.com/blogs/machine-learning/ - type: StatusPage url: https://status.aws.amazon.com/ - type: Support url: https://aws.amazon.com/support/ - type: TermsOfService url: https://aws.amazon.com/service-terms/ - type: PrivacyPolicy url: https://aws.amazon.com/privacy/ - type: Security url: https://docs.aws.amazon.com/sagemaker/latest/dg/security.html - type: Compliance url: https://aws.amazon.com/compliance/ - type: GitHubOrganization url: https://github.com/aws - type: YouTube url: https://www.youtube.com/user/AmazonWebServices - type: StackOverflow url: https://stackoverflow.com/questions/tagged/amazon-sagemaker - type: KnowledgeCenter url: https://repost.aws/knowledge-center - type: CLI url: https://docs.aws.amazon.com/cli/latest/reference/sagemaker/ - type: CLI url: https://github.com/aws/sagemaker-hyperpod-cli title: SageMaker HyperPod CLI - type: SDK url: https://github.com/aws/sagemaker-python-sdk title: Python SDK (GitHub) - type: GitHubRepository url: https://github.com/aws/sagemaker-core - type: GitHubRepository url: https://github.com/aws/sagemaker-distribution - type: SpectralRules url: rules/amazon-sagemaker-spectral-rules.yml - type: Vocabulary url: vocabulary/amazon-sagemaker-vocabulary.yaml - type: Training url: https://aws.amazon.com/training/ - type: Features data: - name: SageMaker Studio description: Fully integrated development environment for ML work with notebooks, debugging, and experiment tracking. - name: SageMaker HyperPod description: Purpose-built infrastructure for distributed training that reduces foundation model training time by up to 40%. - name: SageMaker JumpStart description: Hub providing access to foundation models, pre-built algorithms, and one-click deployment. - name: SageMaker Autopilot description: Automated model creation with complete visibility and transparency. - name: SageMaker Canvas description: No-code visual interface for creating ML models without writing code. - name: SageMaker Feature Store description: Store, share, and manage features for machine learning models. - name: SageMaker Data Wrangler description: Data preparation tool that reduces transformation workflow time significantly. - name: SageMaker Ground Truth description: Incorporates human feedback throughout the ML lifecycle for data labeling. - name: SageMaker Pipelines description: Purpose-built CI/CD service for machine learning workflows. - name: SageMaker Model Monitor description: Automatically detects concept drift and data quality issues in deployed models. - name: SageMaker Clarify description: Provides machine learning explainability and bias detection. - name: SageMaker Experiments description: Streamlines tracking and management of ML experiments. - name: ML Governance description: Access controls and transparency across the full ML lifecycle with audit trails. - type: UseCases data: - name: Generative AI Applications description: Build custom generative AI applications using proprietary data with foundation model fine-tuning. - name: ML Model Development description: Train and deploy ML models across the entire machine learning lifecycle from exploration to production. - name: Data Analytics description: Query and analyze data across unified sources with built-in SQL analytics and data processing. - name: Enterprise AI Governance description: Manage data and AI artifacts with fine-grained security controls and compliance tooling. - name: Computer Vision description: Build and deploy computer vision models for image classification, object detection, and segmentation. - name: Natural Language Processing description: Train and deploy NLP models for text classification, entity recognition, and language generation. - name: Fraud Detection description: Build real-time fraud detection models with low-latency inference endpoints. - name: Predictive Maintenance description: Deploy ML models on edge devices for predictive maintenance use cases. - type: Integrations data: - name: Amazon S3 description: Store training data, model artifacts, and inference outputs in Amazon S3 data lakes. - name: Amazon Redshift description: Zero-ETL integration for near real-time data ingestion from Redshift warehouses. - name: Amazon ECR description: Store and manage Docker containers for custom training and inference environments. - name: AWS Lambda description: Trigger ML inference pipelines and post-processing workflows with Lambda functions. - name: Amazon EventBridge description: Trigger SageMaker pipelines and workflows based on events. - name: AWS Step Functions description: Orchestrate multi-step ML workflows using Step Functions state machines. - name: Apache Iceberg description: Lakehouse architecture supporting Apache Iceberg-compatible data tools. - name: Amazon DataZone description: SageMaker Catalog built on Amazon DataZone for data discovery and governance. - name: Amazon Q Developer description: Natural language assistance integrated into SageMaker Unified Studio. - name: Hugging Face description: Deploy Hugging Face models directly via SageMaker JumpStart. - type: JSONLD url: json-ld/amazon-sagemaker-context.jsonld - type: JSONSchema url: json-schema/amazon-sagemaker-tag-schema.json - type: JSONStructure url: json-structure/amazon-sagemaker-endpoint-structure.json - type: JSONStructure url: json-structure/amazon-sagemaker-model-structure.json - type: JSONStructure url: json-structure/amazon-sagemaker-notebook-instance-structure.json - type: JSONStructure url: json-structure/amazon-sagemaker-tag-structure.json - type: JSONStructure url: json-structure/amazon-sagemaker-training-job-structure.json - type: Example url: examples/amazon-sagemaker-endpoint-example.json - type: Example url: examples/amazon-sagemaker-model-example.json - type: Example url: examples/amazon-sagemaker-notebook-instance-example.json - type: Example url: examples/amazon-sagemaker-tag-example.json - type: Example url: examples/amazon-sagemaker-training-job-example.json - type: Integrations url: https://aws.amazon.com/partners/ maintainer: Kin Lane integrations: - name: Partner Programs - name: Resources - name: Success Stories - name: Work with an AWS Partner - name: AWS Marketplace - name: AWS Partner Central - name: Partner Paths - name: co-sell with AWS