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Amazon SageMaker vs Microsoft Azure Machine Learning Studio comparison

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Featured Review
Find out what your peers are saying about Amazon SageMaker vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2022.
565,689 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The deployment is very good, where you only need to press a few buttons.""Allows you to create API endpoints.""The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

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"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills.""It's good for citizen data scientists, but also, other people can use Python or .NET code.""The initial setup is very simple and straightforward.""Azure's AutoML feature is probably better than the competition.""It's a great option if you are fairly new and don't want to write too much code.""The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.""The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.""The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."

More Microsoft Azure Machine Learning Studio Pros →

Cons
"Lacking in some machine learning pipelines.""Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier.""AI is a new area and AWS needs to have an internship training program available."

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"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system.""I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else.""A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer.""The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team.""It would be nice if the product offered more accessibility in general.""n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces.""The interface is a bit overloaded.""The data preparation capabilities need to be improved."

More Microsoft Azure Machine Learning Studio Cons →

Pricing and Cost Advice
  • "The support costs are 10% of the Amazon fees and it comes by default."
  • More Amazon SageMaker Pricing and Cost Advice →

  • "The licensing cost is very cheap. It's less than $50 a month."
  • "There is a license required for this solution."
  • "I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
  • More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →

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    Questions from the Community
    Top Answer: 
    We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It… more »
    Top Answer: 
    Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with… more »
    Top Answer: 
    The initial setup is very simple and straightforward.
    Top Answer: 
    The licensing cost is very cheap. It's less than $50 a month would costs for multiple users.
    Ranking
    9th
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    Average Words per Review
    505
    Rating
    7.5
    4th
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    16,529
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    14
    Average Words per Review
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    Rating
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    Comparisons
    Also Known As
    AWS SageMaker, SageMaker
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

    Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

    It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

    Microsoft Azure Machine Learning Will Help You:

    • Rapidly build and train models
    • Operationalize at scale
    • Deliver responsible solutions
    • Innovate on a more secure hybrid platform

    With Microsoft Azure Machine Learning You Can:

    • Prepare data: Microsoft Azure Machine Learning Studio offers data labeling, data preparation, and datasets.
    • Build and train models: Includes notebooks, Visual Studio Code and Github, Automated ML, Compute instance, a drag-and-drop designer, open-source libraries and frameworks, customizable dashboards, and experiments
    • Validate and deploy: Manage endpoints, automate machine learning workflows (pipeline CI/CD), optimize models, access pre-built container images, share and track models and data, train and deploy models across multi-cloud and on-premises.
    • Manage and monitor: Track, log, and analyze data, models, and resources; Detect drift and maintain model accuracy; Trace ML artifacts for compliance; Apply quota management and automatic shutdown; Leverage built-in and custom policies for compliance management; Utilize continuous monitoring with Azure Security Center.

    Microsoft Azure Machine Learning Features:

    • Easy & flexible building interface: Execute your machine learning development through the Microsoft Azure Machine Learning Studio using drag-and-drop components that minimize the code development and straightforward configuration of properties. By being so flexible, the solution also helps build, test ,and generate advanced analytics based on the data.
    • Wide range of supported algorithms: Configuration is simple and easy because Microsoft Azure ML offers readily available well-known algorithms. There is also no limit in importing training data, and the solution enables you to fine-tune your data easily, saving money and time and helping you generate more revenue.
    • Easy implementation of web services: Simply drag and drop your data sets and algorithms, and link them together to implement web services. It only requires one click to create and publish the web service, which can be used from any device by passing valid credentials.
    • Great documentation: Microsoft Azure provides full stacks of documentation, such as tutorials, quick starts, references, and many other resources that help you understand how to easily build, manage, deploy, and access machine learning solutions effectively.

    Microsoft Azure Machine Learning Benefits:

    • It is fully integrated with Python and R SDKs.
    • It has an updated drag-and-drop interface, generally known as Azure Machine Learning Designer.
    • It supports MLPipelines, where you can build flexible and modular pipelines to automate workflows.
    • It supports multiple model formats depending upon the job type.
    • It has automated model training and hyperparameter tuning with code-first and no-code options.
    • It supports data labeling projects.

    Reviews from Real Users:

    "The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.” - Channing S.l, Owner at Channing Stowell Associates

    "The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company

    "The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company

    "The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company

    Offer
    Learn more about Amazon SageMaker
    Learn more about Microsoft Azure Machine Learning Studio
    Sample Customers
    DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company24%
    Media Company14%
    Comms Service Provider14%
    Financial Services Firm8%
    REVIEWERS
    Financial Services Firm14%
    Recruiting/Hr Firm14%
    Computer Software Company14%
    Energy/Utilities Company14%
    VISITORS READING REVIEWS
    Computer Software Company24%
    Comms Service Provider19%
    Financial Services Firm6%
    Manufacturing Company6%
    Company Size
    REVIEWERS
    Midsize Enterprise57%
    Large Enterprise43%
    REVIEWERS
    Small Business29%
    Midsize Enterprise14%
    Large Enterprise57%
    Find out what your peers are saying about Amazon SageMaker vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2022.
    565,689 professionals have used our research since 2012.

    Amazon SageMaker is ranked 9th in Data Science Platforms with 3 reviews while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 16 reviews. Amazon SageMaker is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Amazon SageMaker writes "Good deployment and monitoring features, but the interface could use some improvement". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Has the ability to do templating and transfer it so that we can do multiple types of models and data mining". Amazon SageMaker is most compared with Databricks, Dataiku Data Science Studio, Domino Data Science Platform, KNIME and H2O.ai, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Dataiku Data Science Studio, IBM Watson Studio, Alteryx and TensorFlow. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.

    See our list of best Data Science Platforms vendors.

    We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.