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BigML

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BigML vs TensorFlow: What are the differences?

What is BigML? Machine Learning, made simple. Predictive analytics for big data and not-so-big data. BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

What is TensorFlow? Open Source Software Library for Machine Intelligence. 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.

BigML belongs to "Machine Learning as a Service" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".

Decisions about BigML and TensorFlow
Xi Huang
Developer at University of Toronto · | 8 upvotes · 56.4K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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Pros of BigML
Pros of TensorFlow
  • 1
    Ease of use, great REST API and ML workflow automation
  • 25
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    True Portability
  • 9
    Auto-Differentiation
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful

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Cons of BigML
Cons of TensorFlow
    Be the first to leave a con
    • 9
      Hard
    • 6
      Hard to debug
    • 1
      Documentation not very helpful

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    - No public GitHub repository available -

    What is BigML?

    BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

    What is 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.

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    What tools integrate with BigML?
    What tools integrate with TensorFlow?
      No integrations found

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      What are some alternatives to BigML and TensorFlow?
      DataRobot
      It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.
      H2O
      H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
      RapidMiner
      It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
      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.
      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.
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