GraphLab Create
GraphLab Create

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scikit-learn
scikit-learn

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GraphLab Create vs scikit-learn: What are the differences?

GraphLab Create: Machine learning platform that enables data scientists and app developers to easily create intelligent apps at scale. Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful; scikit-learn: Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

GraphLab Create belongs to "Machine Learning as a Service" category of the tech stack, while scikit-learn can be primarily classified under "Machine Learning Tools".

scikit-learn is an open source tool with 36K GitHub stars and 17.6K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

Pros of GraphLab Create
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Cons of GraphLab Create
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    - No public GitHub repository available -

    What is GraphLab Create?

    Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.

    What is scikit-learn?

    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
    What companies use GraphLab Create?
    What companies use scikit-learn?
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      What tools integrate with GraphLab Create?
      What tools integrate with scikit-learn?
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        What are some alternatives to GraphLab Create and scikit-learn?
        TensorFlow
        TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
        Turi Create
        It 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.
        Azure Machine Learning
        Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
        Amazon SageMaker
        A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
        Amazon Machine Learning
        This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
        See all alternatives
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