Amazon SageMaker vs Azure Machine Learning

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Amazon SageMaker
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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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          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.
          NanoNets
          Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
          Algorithms.io
          Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
          FloydHub
          Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.
          Amazon Elastic Inference
          Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
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