Amazon SageMaker vs Azure Machine Learning: What are the differences?
Developers describe Amazon SageMaker as "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. On the other hand, Azure Machine Learning is detailed as "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 SageMaker and Azure Machine Learning can be primarily classified as "Machine Learning as a Service" tools.
Some of the features offered by Amazon SageMaker are:
- 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
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
Microsoft, Bluebeam Software, and Petra are some of the popular companies that use Azure Machine Learning, whereas Amazon SageMaker is used by Zola, SoFi, and Relay42. Azure Machine Learning has a broader approval, being mentioned in 12 company stacks & 8 developers stacks; compared to Amazon SageMaker, which is listed in 12 company stacks and 6 developer stacks.