Compare Amazon SageMaker vs. Microsoft Azure Machine Learning Studio

Amazon SageMaker is ranked 12th in Data Science Platforms with 1 review while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 5 reviews. Amazon SageMaker is rated 6.0, while Microsoft Azure Machine Learning Studio is rated 7.4. The top reviewer of Amazon SageMaker writes "The Random Cut Forest Algorithm is helpful, but the IDE is immature and needs enhancing". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Enables quick creation of models for PoC in predictive analysis, but needs better ensemble modeling". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio and Cloudera Data Science Workbench, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Amazon SageMaker and Alteryx.
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Most Helpful Review
Find out what your peers are saying about Alteryx, Knime, IBM and others in Data Science Platforms. Updated: October 2019.
377,828 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.

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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.MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.The graphical nature of the output makes it very easy to create PowerPoint reports as well.Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently.Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing.

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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.

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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.​It could use to add some more features in data transformation, time series and the text analytics section.Microsoft should also include more examples and tutorials for using this product.​

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Pricing and Cost Advice
Information Not Available
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
12th
Views
4,634
Comparisons
4,322
Reviews
1
Average Words per Review
163
Avg. Rating
6.0
4th
Views
9,085
Comparisons
7,606
Reviews
5
Average Words per Review
353
Avg. Rating
7.4
Top Comparisons
Compared 27% 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 Company33%
Media Company15%
Comms Service Provider9%
Retailer8%
VISITORS READING REVIEWS
Software R&D Company28%
Comms Service Provider19%
Manufacturing Company6%
Media Company5%
Find out what your peers are saying about Alteryx, Knime, IBM and others in Data Science Platforms. Updated: October 2019.
377,828 professionals have used our research since 2012.
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.
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