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	AWS re:Invent 2017 ⓘ linked to: Amazon Web Services re:Invent 2017
deploymentModel	software as a service ⓘ
developer	Amazon Web Services ⓘ
hasFeature	SageMaker Asynchronous Inference ⓘ linked to: Amazon SageMaker SageMaker Autopilot ⓘ linked to: Amazon SageMaker SageMaker Batch Transform ⓘ linked to: Amazon SageMaker SageMaker Canvas ⓘ linked to: Amazon SageMaker SageMaker Clarify ⓘ linked to: Amazon SageMaker SageMaker Data Wrangler ⓘ linked to: Amazon SageMaker SageMaker Debugger ⓘ linked to: Amazon SageMaker SageMaker Distributed Data Parallel ⓘ SageMaker Domain ⓘ linked to: Amazon SageMaker SageMaker Edge Manager ⓘ linked to: Amazon SageMaker SageMaker Experiments ⓘ linked to: Amazon SageMaker SageMaker Feature Store ⓘ linked to: Amazon SageMaker SageMaker Ground Truth ⓘ linked to: Amazon SageMaker SageMaker Hyperparameter Tuning Jobs ⓘ linked to: Amazon SageMaker SageMaker Inference Endpoints ⓘ linked to: Amazon SageMaker SageMaker JumpStart ⓘ linked to: Amazon SageMaker SageMaker Local Mode ⓘ linked to: Amazon SageMaker SageMaker Model Monitor ⓘ linked to: Amazon SageMaker SageMaker Model Parallelism ⓘ SageMaker Model Registry ⓘ linked to: Amazon SageMaker SageMaker Multi-container Endpoints ⓘ SageMaker Multi-model Endpoints ⓘ linked to: Amazon SageMaker SageMaker Neo ⓘ linked to: Amazon SageMaker SageMaker Notebook Instances ⓘ linked to: Amazon SageMaker SageMaker Pipelines ⓘ linked to: Amazon SageMaker SageMaker Processing Jobs ⓘ linked to: Amazon SageMaker SageMaker Profiler ⓘ SageMaker Projects ⓘ linked to: Amazon SageMaker SageMaker Real-time Inference ⓘ SageMaker SDK for Python ⓘ linked to: Amazon SageMaker SageMaker Serverless Inference ⓘ SageMaker Studio ⓘ SageMaker Studio Lab ⓘ linked to: SageMaker Studio SageMaker Studio Notebooks ⓘ linked to: SageMaker Studio SageMaker Training Compiler ⓘ linked to: Amazon SageMaker SageMaker Training Jobs ⓘ linked to: Amazon SageMaker SageMaker Training on Spot Instances ⓘ linked to: Amazon SageMaker
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.com, Inc. ⓘ linked to: Amazon
partOf	Amazon Web Services ⓘ
provider	Amazon Web Services ⓘ
regionAvailability	multiple AWS regions worldwide ⓘ
runsOn	AWS cloud infrastructure ⓘ linked to: Amazon Web Services
supportsFramework	Apache MXNet ⓘ linked to: MXNet CatBoost ⓘ Hugging Face Transformers ⓘ LightGBM ⓘ PyTorch ⓘ Scikit-learn ⓘ linked to: 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

Referenced by (33)

Full triples — surface form annotated when it differs from this entity's canonical label.

Amazon Web Services → offersService → Amazon SageMaker ⓘ

MXNet → integratedInto → Amazon SageMaker ⓘ

Amazon SageMaker → hasFeature → SageMaker Notebook Instances ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Training Jobs ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Inference Endpoints ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Batch Transform ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Processing Jobs ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Pipelines ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Experiments ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Debugger ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Model Monitor ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Autopilot ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Ground Truth ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Clarify ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Feature Store ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker JumpStart ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Neo ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Edge Manager ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Canvas ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Data Wrangler ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Model Registry ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Domain ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Projects ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Training Compiler ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Hyperparameter Tuning Jobs ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Asynchronous Inference ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Multi-model Endpoints ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Training on Spot Instances ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker Local Mode ⓘ

linked to: Amazon SageMaker

Amazon SageMaker → hasFeature → SageMaker SDK for Python ⓘ

linked to: Amazon SageMaker

AWS Key Management Service → integratesWith → Amazon SageMaker ⓘ

AWS Step Functions → integratesWith → Amazon SageMaker ⓘ

Amazon Neptune → integratesWith → Amazon SageMaker ⓘ

All labels observed (29)

How this entity was disambiguated Show

Statements (90)

How these facts were elicited Show

Referenced by (33)

How this entity was disambiguated

How these facts were elicited