Turi Create
E732972
Turi Create is an open-source Python library from Apple that simplifies building, training, and deploying machine learning models, especially for use with Apple’s Core ML framework.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Turi Create canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8415086 — 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: Turi Create Context triple: [Core ML, integratesWith, Turi Create]
-
A.
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.
-
B.
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.
-
C.
Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
-
D.
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.
-
E.
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.
- 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: Turi Create Target entity description: Turi Create is an open-source Python library from Apple that simplifies building, training, and deploying machine learning models, especially for use with Apple’s Core ML framework.
-
A.
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.
-
B.
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.
-
C.
Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
-
D.
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.
-
E.
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.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
machine learning library ⓘ open-source software ⓘ |
| category | Apple machine learning tools ⓘ |
| designedFor | integration with Core ML ⓘ |
| developer | Apple Inc. ⓘ |
| documentation | https://apple.github.io/turicreate/docs/ ⓘ |
| feature |
automatic model selection utilities
ⓘ
data exploration tools ⓘ export to Core ML models ⓘ high-level APIs for model training ⓘ model evaluation tools ⓘ out-of-core computation via SFrame ⓘ support for large datasets ⓘ |
| integratesWith |
Core ML
NERFINISHED
ⓘ
Jupyter Notebook NERFINISHED ⓘ |
| license | BSD-style license ⓘ |
| platform |
Linux
ⓘ
macOS ⓘ |
| primaryUse |
building machine learning models
ⓘ
deploying machine learning models ⓘ training machine learning models ⓘ |
| programmingLanguage | Python ⓘ |
| repository | https://github.com/apple/turicreate ⓘ |
| requires |
Python 2.7
ⓘ
Python 3.x ⓘ |
| supports |
SArray data structure
ⓘ
SFrame data structure ⓘ SGraph data structure ⓘ activity classification models ⓘ classification models ⓘ clustering models ⓘ graph analytics ⓘ image classification models ⓘ nearest neighbors models ⓘ object detection models ⓘ recommender systems ⓘ regression models ⓘ text classification models ⓘ |
| supportsFormat | Core ML model format ⓘ |
| supportsTask |
image similarity
ⓘ
object detection ⓘ recommendation ⓘ sentiment analysis ⓘ style transfer ⓘ |
| targetUser |
Python developers
ⓘ
iOS app developers ⓘ machine learning practitioners ⓘ |
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: Turi Create Description of subject: Turi Create is an open-source Python library from Apple that simplifies building, training, and deploying machine learning models, especially for use with Apple’s Core ML framework.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.