Amazon Personalize vs Amazon SageMaker

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Amazon Personalize

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Amazon SageMaker

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Amazon Personalize vs Amazon SageMaker: 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 Amazon SageMaker? Accelerated Machine Learning. 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 Personalize and Amazon SageMaker 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, Amazon SageMaker provides the following key features:

  • Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
  • Train: one-click training, authentic model tuning
  • Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling
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What is Amazon Personalize?

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

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.

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

What companies use Amazon Personalize?
What companies use Amazon SageMaker?
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What tools integrate with Amazon Personalize?
What tools integrate with Amazon SageMaker?

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What are some alternatives to Amazon Personalize and Amazon SageMaker?
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.
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.
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.
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.
See all alternatives