Create ML
E732968
Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Create ML canonical | 5 |
| Create ML app | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8415004 — 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: Create ML Context triple: [Apple Neural Engine, accessibleVia, Create ML]
-
A.
Oracle Machine Learning
Oracle Machine Learning is a suite of in-database machine learning algorithms and tools from Oracle that enables data scientists and analysts to build, deploy, and manage predictive models directly within Oracle databases.
-
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.
AutoML
AutoML is a set of machine learning tools and services that automatically build, train, and optimize models with minimal manual coding or expertise.
-
D.
ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
-
E.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
- 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: Create ML Target entity description: Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
-
A.
Oracle Machine Learning
Oracle Machine Learning is a suite of in-database machine learning algorithms and tools from Oracle that enables data scientists and analysts to build, deploy, and manage predictive models directly within Oracle databases.
-
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.
AutoML
AutoML is a set of machine learning tools and services that automatically build, train, and optimize models with minimal manual coding or expertise.
-
D.
ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
-
E.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
- F. None of above. chosen
Statements (55)
| Predicate | Object |
|---|---|
| instanceOf |
Apple software
ⓘ
machine learning tool ⓘ |
| category |
developer tool
ⓘ
machine learning framework ⓘ model training tool ⓘ |
| designedFor | macOS developers ⓘ |
| developer | Apple Inc. ⓘ |
| distribution |
Mac App Store
ⓘ
linked to:
App Store
|
| exportsTo |
Core ML model format
ⓘ
linked to:
Core ML
|
| feature |
automatic model training
ⓘ
data augmentation options ⓘ drag-and-drop dataset setup ⓘ hyperparameter configuration ⓘ model evaluation tools ⓘ model preview ⓘ training progress visualization ⓘ |
| goal | simplify machine learning model creation on Apple platforms ⓘ |
| inputFormat |
audio
ⓘ
images ⓘ tabular data ⓘ text ⓘ |
| integratesWith |
Core ML
ⓘ
Create ML framework ⓘ Swift ⓘ Xcode ⓘ |
| license | proprietary ⓘ |
| operatingSystem | macOS ⓘ |
| platform | Apple ecosystem ⓘ |
| primaryLanguage | Swift ⓘ |
| provides |
Swift API
ⓘ
command-line interface ⓘ graphical user interface ⓘ |
| requires | macOS ⓘ |
| runsOnDevice | Mac ⓘ |
| supports |
activity classification
ⓘ
image classification ⓘ image similarity ⓘ object detection ⓘ on-device training ⓘ recommendation models ⓘ regression ⓘ sound classification ⓘ style transfer ⓘ supervised learning ⓘ tabular data classification ⓘ text classification ⓘ transfer learning ⓘ word tagging ⓘ |
| supportsLanguage | SwiftUI integration ⓘ |
| targetUser |
app developers
ⓘ
data scientists on macOS ⓘ |
| useCase |
building custom ML models for iOS apps
ⓘ
building custom ML models for macOS apps ⓘ building custom ML models for tvOS apps ⓘ building custom ML models for watchOS apps ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Create ML Description of subject: Create ML is Apple's machine learning tool that lets developers easily build and train models directly on macOS using simple, user-friendly interfaces.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.