{
  "slug": "amazon-web-services-aws-sagemaker",
  "name": "Amazon Web Services (AWS) SageMaker业务",
  "description": "Amazon Web Services (AWS) SageMaker is a fully managed cloud service that provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly. It removes the heavy lifting from each step of the machine learning process to make it easier to develop high-quality models. As a modular service, it covers the entire ML lifecycle—from data labeling and preparation to hosting and monitoring.",
  "url": "https://optimly.ai/brand/amazon-web-services-aws-sagemaker",
  "logoUrl": "",
  "baiScore": 94,
  "archetype": "Challenger",
  "category": "Cloud Computing",
  "categorySlug": null,
  "keyFacts": [],
  "aiReadiness": [],
  "competitors": [
    {
      "slug": "azure-machine-learning",
      "name": "Azure Machine Learning"
    },
    {
      "slug": "databricks",
      "name": "Databricks"
    },
    {
      "slug": "datarobot",
      "name": "DataRobot"
    },
    {
      "slug": "google-cloud-vertex-ai",
      "name": "Google Cloud Vertex AI"
    }
  ],
  "inboundCompetitors": [
    {
      "slug": "google-vertex-ai",
      "name": "Google Vertex AI"
    },
    {
      "slug": "microsoft-azure-ai",
      "name": "Microsoft Azure AI"
    }
  ],
  "aiAlternatives": [
    {
      "slug": "bare-metalvm-diy",
      "name": "Bare Metalvm Diy"
    }
  ],
  "parentBrand": {
    "slug": "amazon-web-services-aws",
    "name": "Amazon Web Services (AWS)"
  },
  "subBrands": [],
  "updatedAt": "2026-04-10T18:04:43.758+00:00",
  "verifiedVitals": {
    "website": "https://aws.amazon.com/sagemaker/",
    "founded": "2017",
    "headquarters": "Seattle, WA, USA",
    "pricing_model": "Usage-based (Pay-as-you-go)",
    "core_products": "SageMaker Studio, SageMaker Canvas (No-code), SageMaker Ground Truth (Data labeling), SageMaker Pipelines (MLOps)",
    "key_differentiator": "The deepest integration with the AWS ecosystem, offering a complete end-to-end ML lifecycle from data prep to edge deployment in a single managed environment.",
    "target_markets": "Enterprise data science teams, ML engineers, software developers, and business analysts.",
    "employee_count": "Not publicly available",
    "funding_stage": "Not publicly available",
    "subcategory": "Machine Learning Platforms"
  },
  "timestamp": 1775988051436
}