Compare H2O.ai vs. Microsoft Azure Machine Learning Studio

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Most Helpful Review
Find out what your peers are saying about H2O.ai vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2021.
455,301 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 most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

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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
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

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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
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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."

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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
14th
Views
7,585
Comparisons
5,012
Reviews
4
Average Words per Review
315
Rating
7.3
5th
Views
13,868
Comparisons
11,097
Reviews
10
Average Words per Review
561
Rating
7.6
Popular Comparisons
Compared 18% of the time.
Compared 15% of the time.
Compared 10% of the time.
Compared 9% of the time.
Also Known As
Azure Machine Learning
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H2O.ai
Microsoft
Overview

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

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 H2O.ai
Learn more about Microsoft Azure Machine Learning Studio
Sample Customers
poder.io, Stanley Black & Decker, G5, PWC, Comcast, CiscoWalgreens Boots Alliance, Schneider Electric, BP
Top Industries
VISITORS READING REVIEWS
Computer Software Company33%
Comms Service Provider14%
Media Company6%
Insurance Company6%
VISITORS READING REVIEWS
Computer Software Company29%
Comms Service Provider16%
Energy/Utilities Company6%
Manufacturing Company6%
Company Size
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
REVIEWERS
Small Business46%
Midsize Enterprise8%
Large Enterprise46%
Find out what your peers are saying about H2O.ai vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2021.
455,301 professionals have used our research since 2012.

H2O.ai is ranked 14th in Data Science Platforms with 2 reviews while Microsoft Azure Machine Learning Studio is ranked 5th in Data Science Platforms with 10 reviews. H2O.ai is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of H2O.ai writes "Good collaboration functionality, but better integration with Python for data science is needed". 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". H2O.ai is most compared with KNIME, Amazon SageMaker, Dataiku Data Science Studio, Alteryx and Databricks, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx, IBM Watson Studio, Amazon SageMaker and IBM SPSS Modeler. See our H2O.ai vs. Microsoft Azure Machine Learning Studio report.

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