Amazon Personalize聽vs聽Azure Machine Learning

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Amazon Personalize vs Azure Machine Learning: 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 Azure Machine Learning? 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.

Amazon Personalize and Azure Machine Learning can be primarily classified 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, Azure Machine Learning provides the following key features:

  • Designed for new and experienced users
  • Proven algorithms from MS Research, Xbox and Bing
  • First class support for the open source language R
- No public GitHub repository available -
- 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 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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            What are some alternatives to Amazon Personalize and Azure Machine Learning?
            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鈥檚 machine learning expertise to mobile developers in a powerful and easy-to-use package.
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