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User Guide Overview

Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

As you use Turi Create in your work, reference this guide to understand:

If you haven’t already installed Turi Create, you can find instructionshere.

SFrame

SFrame is a scalable, tabular, column-mutable dataframe object. The data in SFrame is stored column-wise, and is stored on persistent storage (e.g. disk) to avoid being constrained by memory size. Each column in an SFrame is a size-immutable SArray, but SFrames are mutable in that columns can be added and subtracted with ease. An SFrame essentially acts as an ordered dict of SArrays.

Currently, we support constructing an SFrame from the following data formats: .csv (comma separated value) file, SFrame directory archive (A directory where an Sframe was saved previously), general text file (with csv parsing options; see read_csv()), Python dictionary, pandas.DataFrame and JSON.

An SFrame can be constructed with data from your local file system, a network file system mounted locally, HDFS, Amazon S3, or HTTP(S).

See Working with data for more guidance on data structures and the API docsfor a more complete reference.

Models

Turi Create offers a broad set of packaged application based toolkits as well as algorithms for model creation.

Start with the task you want to accomplish

Application-oriented toolkits in Turi Create offer default parameters, building blocks and baseline models that help you get started quickly with their dataset without sacrificing the ability to go back and customize models later. Each incorporates automatic feature engineering and model selection.

Using these toolkits, you can tackle a number of common scenarios:

Machine learning essentials

Essential machine learning models, organized into algorithm-based toolkits:

Refer to the Machine Learning API documentationfor complete algorithms and toolkits.

Deployment to Core ML

With Core ML, you can integrate machine learning models into your macOS, iOS, watchOS, and tvOS app. Many models created in Turi Create can be exported for use in Core ML.

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