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Amazon Machine Learning vs Azure Machine Learning: What are the differences?
Amazon Machine Learning: Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology. 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; 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 Machine Learning and Azure Machine Learning can be primarily classified as "Machine Learning as a Service" tools.
Some of the features offered by Amazon Machine Learning are:
- Easily Create Machine Learning Models
- From Models to Predictions in Seconds
- Scalable, High Performance Prediction Generation Service
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
According to the StackShare community, Azure Machine Learning has a broader approval, being mentioned in 12 company stacks & 8 developers stacks; compared to Amazon Machine Learning, which is listed in 8 company stacks and 9 developer stacks.