Amazon SageMaker vs Azure Machine Learning

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Amazon SageMaker

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Azure Machine Learning

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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.

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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.

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 SageMaker and Azure Machine Learning?
Amazon Machine Learning
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.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
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.
IBM Watson
It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.
See all alternatives