Compare Amazon SageMaker vs. Microsoft Azure Machine Learning Studio

Amazon SageMaker is ranked 14th in Data Science Platforms with 3 reviews while Microsoft Azure Machine Learning Studio is ranked 7th in Data Science Platforms with 9 reviews. Amazon SageMaker is rated 7.4, while Microsoft Azure Machine Learning Studio is rated 7.4. 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, Microsoft Azure Machine Learning Studio and Domino Data Science Platform, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx and Amazon SageMaker. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.
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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 2020.
396,781 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.They are doing a good job of evolving.The few projects we have done have been promising.

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The UI is very user-friendly and that AI is easy to use.The most valuable feature is data normalization.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 most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.Visualisation, and the possibility of sharing functions are key features.It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.When you import the dataset you can see the data distribution easily with graphics and statistical measures.Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most.

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Cons
Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier.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.I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.

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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 data cleaning functionality is something that could be better and needs to be improved.Integration with social media would be a valuable enhancement.If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.Operability with R could be improved.I would like to see modules to handle Deep Learning frameworks.I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated.Enable creating ensemble models easier, adding more machine learning algorithms.

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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.

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From a developer's perspective, I find the price of this solution high.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.To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.

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Ranking
14th
Views
6,086
Comparisons
5,582
Reviews
2
Average Words per Review
517
Avg. Rating
7.5
7th
Views
10,077
Comparisons
8,352
Reviews
8
Average Words per Review
479
Avg. Rating
7.3
Top Comparisons
Compared 30% 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, Intuit
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Top Industries
VISITORS READING REVIEWS
Software R&D Company32%
Media Company15%
Comms Service Provider12%
Retailer6%
VISITORS READING REVIEWS
Software R&D Company30%
Comms Service Provider18%
Media Company6%
Manufacturing Company5%
Find out what your peers are saying about Amazon SageMaker vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
396,781 professionals have used our research since 2012.
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