Compare H2O.ai vs. SAS Enterprise Miner

Cancel
You must select at least 2 products to compare!
H2O.ai Logo
7,624 views|5,023 comparisons
SAS Enterprise Miner Logo
3,941 views|2,986 comparisons
Most Helpful Review
Find out what your peers are saying about H2O.ai vs. SAS Enterprise Miner and other solutions. Updated: September 2020.
442,517 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
"It is helpful, intuitive, and easy to use. The learning curve is not too steep.""The ease of use in connecting to our cluster machines.""One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm.""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."

More H2O.ai Pros »

"The setup is straightforward. Deployment doesn't take more than 30 minutes.""The solution is very good for data mining or any mining issues.""he solution is scalable.""Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic.""The most valuable feature is the decision tree creation.""The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them.""Good data management and analytics.""The solution is able to handle quite large amounts of data beautifully."

More SAS Enterprise Miner Pros »

Cons
"The model management features could be improved.""I would like to see more features related to deployment.""The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability.""On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

More H2O.ai Cons »

"The user interface of the solution needs improvement. It needs to be more visual.""The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch.""The solution needs an easier interface for the user. The user experience isn't so easy for our clients.""Virtualization could be much better.""The ease of use can be improved. When you are new it seems a bit complex.""The visualization of the models is not very attractive, so the graphics should be improved.""Technical support could be improved.""While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."

More SAS Enterprise Miner Cons »

Pricing and Cost Advice
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

More H2O.ai Pricing and Cost Advice »

"This solution is for large corporations because not everybody can afford it."

More SAS Enterprise Miner Pricing and Cost Advice »

report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
442,517 professionals have used our research since 2012.
Questions from the Community
Top Answer: 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.
Top Answer: On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time. It becomes a problem. I would like to see better integration with Python… more »
Top Answer: I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them. There are various… more »
Top Answer: The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them.
Top Answer: The license is really expensive. This solution is for large corporations because not everybody can afford it. It is a little bit tricky because you have to buy a license for each and every component… more »
Top Answer: The visualization of the models is not very attractive, so the graphics should be improved. I would like to see the user interface improved a bit.
Ranking
12th
Views
7,624
Comparisons
5,023
Reviews
5
Average Words per Review
350
Avg. Rating
7.6
10th
Views
3,941
Comparisons
2,986
Reviews
8
Average Words per Review
389
Avg. Rating
7.8
Popular Comparisons
Compared 19% of the time.
Compared 15% of the time.
Compared 1% of the time.
Compared 11% of the time.
Also Known As
Enterprise Miner
Learn
H2O.ai
SAS
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.

SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data. Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.
Offer
Learn more about H2O.ai
Learn more about SAS Enterprise Miner
Sample Customers
poder.io, Stanley Black & Decker, G5, PWC, Comcast, CiscoGenerali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare
Top Industries
VISITORS READING REVIEWS
Computer Software Company36%
Comms Service Provider13%
Media Company8%
Insurance Company8%
VISITORS READING REVIEWS
Computer Software Company34%
Media Company9%
Insurance Company8%
Financial Services Firm7%
Company Size
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
REVIEWERS
Small Business20%
Midsize Enterprise40%
Large Enterprise40%
Find out what your peers are saying about H2O.ai vs. SAS Enterprise Miner and other solutions. Updated: September 2020.
442,517 professionals have used our research since 2012.
H2O.ai is ranked 12th in Data Science Platforms with 5 reviews while SAS Enterprise Miner is ranked 10th in Data Science Platforms with 8 reviews. H2O.ai is rated 7.6, while SAS Enterprise Miner is rated 7.8. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". On the other hand, the top reviewer of SAS Enterprise Miner writes "Good GUI, an easy initial setup, and very flexible". H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and SAS Visual Analytics, whereas SAS Enterprise Miner is most compared with IBM SPSS Modeler, RapidMiner, SAS Visual Analytics, Amazon SageMaker and FICO Model Builder. See our H2O.ai vs. SAS Enterprise Miner 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.