Amazon SageMaker

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