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Amazon Personalize
Amazon Personalize

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BigML
BigML

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Amazon Personalize vs BigML: What are the differences?

What is Amazon Personalize? Real-time personalization and recommendation. Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

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.

Amazon Personalize and BigML can be categorized as "Machine Learning as a Service" tools.

Some of the features offered by Amazon Personalize are:

  • Combine customer and contextual data to generate high-quality recommendations
  • Automated machine learning
  • Continuous learning to improve performance

On the other hand, BigML provides the following key features:

  • REST API
  • bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more
  • several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more
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- No public GitHub repository available -

What is Amazon Personalize?

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

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.
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            What are some alternatives to Amazon Personalize and BigML?
            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.
            Keras
            Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
            scikit-learn
            scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
            PyTorch
            PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
            ML Kit
            ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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