Azure Machine Learning vs GraphLab Create

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Azure Machine Learning

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GraphLab Create

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Azure Machine Learning vs GraphLab Create: What are the differences?

Developers describe Azure Machine Learning as "A fully-managed cloud service for predictive analytics". 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. On the other hand, GraphLab Create is detailed as "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.

Azure Machine Learning and GraphLab Create can be categorized as "Machine Learning as a Service" tools.

Some of the features offered by Azure Machine Learning are:

  • Designed for new and experienced users
  • Proven algorithms from MS Research, Xbox and Bing
  • First class support for the open source language R

On the other hand, GraphLab Create provides the following key features:

  • Analyze terabyte scale data at interactive speeds, on your desktop.
  • A Single platform for tabular data, graphs, text, and images.
  • State of the art machine learning algorithms including deep learning, boosted trees, and factorization machines.
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Pros of Azure Machine Learning
Pros of GraphLab Create
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      Intelligent Function Defaults
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      Fast Data Summary
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      Simple Machine Learning Tools

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    What is 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.

    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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Azure Machine Learning?
    What companies use GraphLab Create?
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      What tools integrate with Azure Machine Learning?
      What tools integrate with GraphLab Create?
        No integrations found
        What are some alternatives to Azure Machine Learning and GraphLab Create?
        Python
        Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
        Azure Databricks
        Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
        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.
        Databricks
        Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
        See all alternatives