Amazon Machine Learning vs Amazon SageMaker vs GraphLab Create

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Amazon Machine Learning

166
246
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Amazon SageMaker

277
271
+ 1
0
GraphLab Create

8
40
+ 1
3
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Pros of Amazon Machine Learning
Pros of Amazon SageMaker
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 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.

      What is 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.

      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.

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      Jobs that mention Amazon Machine Learning, Amazon SageMaker, and GraphLab Create as a desired skillset
      What companies use Amazon Machine Learning?
      What companies use Amazon SageMaker?
      What companies use GraphLab Create?
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        What tools integrate with Amazon Machine Learning?
        What tools integrate with Amazon SageMaker?
        What tools integrate with GraphLab Create?
          No integrations found
            No integrations found

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            What are some alternatives to Amazon Machine Learning, Amazon SageMaker, and GraphLab Create?
            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.
            Apache Spark
            Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
            RapidMiner
            It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
            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.
            Google AI Platform
            Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.
            See all alternatives