Compare Amazon SageMaker vs. Microsoft Azure Machine Learning Studio

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Most Helpful Review
Find out what your peers are saying about Amazon SageMaker vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2021.
455,108 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 few projects we have done have been promising.""They are doing a good job of evolving.""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.""Allows you to create API endpoints.""The deployment is very good, where you only need to press a few buttons."

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"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.""The most valuable feature is data normalization.""The UI is very user-friendly and that AI is easy to use.""The solution is very fast and simple for a data science solution.""Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills.""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 AutoML is helpful when you're starting to explore the problem that you're trying to solve.""The interface is very intuitive."

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Cons
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.""I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox.""AI is a new area and AWS needs to have an internship training program available.""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."

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"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.""The data cleaning functionality is something that could be better and needs to be improved.""When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers.""The solution should be more customizable. There should be more algorithms.""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.""Integration with social media would be a valuable enhancement.""The AutoML feature is very basic and they should improve it by using a more robust algorithm.""The data preparation capabilities need to be improved."

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Pricing and Cost Advice
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation.""The support costs are 10% of the Amazon fees and it comes by default."

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"When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly.""From a developer's perspective, I find the price of this solution high."

More Microsoft Azure Machine Learning Studio Pricing and Cost Advice »

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Questions from the Community
Top Answer: Allows you to create API endpoints.
Top Answer: The pricing for the Notebook endpoints is a bit high, but generally reasonable.
Top Answer: The product has come a long way and they've added a lot of things, but in terms of improvement I would like to probably have features such as MLflow embedded into it. Additional features I would like… more »
Top Answer: Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills.
Top Answer: The pricing and licensing are difficult to explain to clients. Their rationale for what things cost and why are not easy to explain.
Top Answer: I used Azure Machine Learning in a free trial and I had a complete preview of the service. A problem that I encountered was that I had a model that I wanted to deploy and use on Azure Machine… more »
Ranking
13th
Views
11,164
Comparisons
9,675
Reviews
4
Average Words per Review
510
Rating
7.5
5th
Views
13,868
Comparisons
11,097
Reviews
10
Average Words per Review
561
Rating
7.6
Popular Comparisons
Compared 28% of the time.
Compared 8% of the time.
Compared 5% of the time.
Also Known As
AWS SageMaker, SageMakerAzure Machine Learning
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Amazon
Microsoft
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.

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, IntuitWalgreens Boots Alliance, Schneider Electric, BP
Top Industries
VISITORS READING REVIEWS
Computer Software Company29%
Media Company18%
Comms Service Provider11%
Insurance Company5%
VISITORS READING REVIEWS
Computer Software Company29%
Comms Service Provider16%
Energy/Utilities Company6%
Manufacturing Company6%
Company Size
REVIEWERS
Midsize Enterprise57%
Large Enterprise43%
REVIEWERS
Small Business46%
Midsize Enterprise8%
Large Enterprise46%
Find out what your peers are saying about Amazon SageMaker vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2021.
455,108 professionals have used our research since 2012.

Amazon SageMaker is ranked 13th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 5th in Data Science Platforms with 10 reviews. Amazon SageMaker is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Amazon SageMaker writes "A solution with great computational storage, has many pre-built models, is stable, and has good support". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Amazon SageMaker is most compared with Databricks, Dataiku Data Science Studio, H2O.ai, Domino Data Science Platform and KNIME, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx, IBM Watson Studio, Dataiku Data Science Studio and KNIME. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.

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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.