Amazon SageMaker

E293756

Amazon Web Services service cloud machine learning platform managed service

Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.

Try in SPARQL Jump to: Surface forms Disambiguation Statements Elicitation Referenced by

All labels observed (29)

Label	Occurrences
Amazon SageMaker canonical	5
SageMaker Asynchronous Inference	1
SageMaker Autopilot	1
SageMaker Batch Transform	1
SageMaker Canvas	1
SageMaker Clarify	1
SageMaker Data Wrangler	1
SageMaker Debugger	1
SageMaker Domain	1
SageMaker Edge Manager	1
SageMaker Experiments	1
SageMaker Feature Store	1
SageMaker Ground Truth	1
SageMaker Hyperparameter Tuning Jobs	1
SageMaker Inference Endpoints	1
SageMaker JumpStart	1
SageMaker Local Mode	1
SageMaker Model Monitor	1
SageMaker Model Registry	1
SageMaker Multi-model Endpoints	1
SageMaker Neo	1
SageMaker Notebook Instances	1
SageMaker Pipelines	1
SageMaker Processing Jobs	1
SageMaker Projects	1
SageMaker SDK for Python	1
SageMaker Training Compiler	1
SageMaker Training Jobs	1
SageMaker Training on Spot Instances	1

How this entity was disambiguated

This entity first appeared as the object of triple T2714025 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Amazon SageMaker
Context triple: [Amazon Web Services, offersService, Amazon SageMaker]

A. Landing AI
Landing AI is a technology company focused on making artificial intelligence accessible to traditional industries by helping them build and deploy practical AI solutions, particularly in manufacturing and computer vision.
B. Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
C. Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
D. AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service from Amazon Web Services that simplifies data preparation and integration for analytics and data warehousing.
E. Einstein AI
Einstein AI is Salesforce’s integrated artificial intelligence platform that powers predictive analytics, automation, and intelligent insights across its CRM ecosystem.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Amazon SageMaker
Target entity description: Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.

A. Landing AI
Landing AI is a technology company focused on making artificial intelligence accessible to traditional industries by helping them build and deploy practical AI solutions, particularly in manufacturing and computer vision.
B. Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
C. Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
D. AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service from Amazon Web Services that simplifies data preparation and integration for analytics and data warehousing.
E. Einstein AI
Einstein AI is Salesforce’s integrated artificial intelligence platform that powers predictive analytics, automation, and intelligent insights across its CRM ecosystem.
F. None of above. chosen

Statements (90)

Predicate	Object
instanceOf	Amazon Web Services service ⓘ cloud machine learning platform ⓘ managed service ⓘ
accessModel	pay-as-you-go ⓘ
announcedAt	Amazon Web Services re:Invent 2017 ⓘ surface form: AWS re:Invent 2017
deploymentModel	software as a service ⓘ
developer	Amazon Web Services ⓘ
hasFeature	Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Asynchronous Inference Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Autopilot Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Batch Transform Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Canvas Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Clarify Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Data Wrangler Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Debugger SageMaker Distributed Data Parallel ⓘ Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Domain Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Edge Manager Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Experiments Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Feature Store Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Ground Truth Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Hyperparameter Tuning Jobs Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Inference Endpoints Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker JumpStart Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Local Mode Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Model Monitor SageMaker Model Parallelism ⓘ Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Model Registry SageMaker Multi-container Endpoints ⓘ Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Multi-model Endpoints Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Neo Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Notebook Instances Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Pipelines Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Processing Jobs SageMaker Profiler ⓘ Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Projects SageMaker Real-time Inference ⓘ Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker SDK for Python SageMaker Serverless Inference ⓘ SageMaker Studio ⓘ SageMaker Studio ⓘ surface form: SageMaker Studio Lab SageMaker Studio ⓘ surface form: SageMaker Studio Notebooks Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Training Compiler Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Training Jobs Amazon SageMaker self-linksurface differs ⓘ surface form: SageMaker Training on Spot Instances
industry	cloud computing ⓘ machine learning ⓘ
integratesWith	AWS CloudTrail ⓘ AWS Glue ⓘ AWS Identity and Access Management ⓘ AWS Key Management Service ⓘ AWS Lambda ⓘ AWS Step Functions ⓘ Amazon CloudWatch ⓘ Amazon ECR ⓘ Amazon EMR ⓘ Amazon Redshift ⓘ Amazon S3 ⓘ Amazon VPC ⓘ
launchDate	2017-11 ⓘ
operatedBy	Amazon Web Services ⓘ
owner	Amazon ⓘ surface form: Amazon.com, Inc.
partOf	Amazon Web Services ⓘ
provider	Amazon Web Services ⓘ
regionAvailability	multiple AWS regions worldwide ⓘ
runsOn	Amazon Web Services ⓘ surface form: AWS cloud infrastructure
supportsFramework	MXNet ⓘ surface form: Apache MXNet CatBoost ⓘ Hugging Face Transformers ⓘ LightGBM ⓘ PyTorch ⓘ scikit-learn ⓘ surface form: Scikit-learn TensorFlow ⓘ XGBoost ⓘ
supportsLanguage	Python ⓘ R ⓘ
supportsStandard	Docker ⓘ Kubernetes-compatible containers ⓘ
supportsUseCase	MLOps ⓘ automated machine learning ⓘ batch inference ⓘ data labeling ⓘ explainable AI ⓘ feature engineering ⓘ model deployment ⓘ model monitoring ⓘ model training ⓘ real-time inference ⓘ
targetUser	data scientists ⓘ developers ⓘ machine learning engineers ⓘ

How these facts were elicited