Compare H2O.ai vs. IBM Watson Studio

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H2O.ai Logo
7,545 views|4,988 comparisons
IBM Watson Studio Logo
5,118 views|4,063 comparisons
Most Helpful Review
Find out what your peers are saying about H2O.ai vs. IBM Watson Studio and other solutions. Updated: September 2020.
438,043 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.""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 ease of use in connecting to our cluster machines.""It is helpful, intuitive, and easy to use. The learning curve is not too steep."

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"It has a lot of data connectors, which is extremely helpful.""IBM Watson Studio consistently automates across channels.""The system's ability to take a look at data, segment it and then use that data very differently.""The scalability of IBM Watson Studio is great.""The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video.""The solution is very easy to use."

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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.""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.""I would like to see more features related to deployment.""The model management features could be improved."

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"The initial setup was complex.""Some of the solutions are really good solutions but they can be a little too costly for many.""It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs.""The decision making in their decision making feature is less good than other options.""So a better user interface could be very helpful""More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."

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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."

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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: It has a lot of data connectors, which is extremely helpful.
Top Answer: It will come down again to cost. Some of the solutions are really good solutions but they can be a little too costly for many. I think a lot of software vendors have considered having special pricing… more »
Top Answer: The initial setup was complex.
Ranking
11th
Views
7,545
Comparisons
4,988
Reviews
5
Average Words per Review
350
Avg. Rating
7.6
9th
Views
5,118
Comparisons
4,063
Reviews
5
Average Words per Review
468
Avg. Rating
8.2
Popular Comparisons
Compared 19% of the time.
Compared 15% of the time.
Compared 4% of the time.
Also Known As
Watson Studio, IBM Data Science Experience, Data Science Experience, DSx
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H2O.ai
IBM
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.

IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

Offer
Learn more about H2O.ai
Learn more about IBM Watson Studio
Sample Customers
poder.io, Stanley Black & Decker, G5, PWC, Comcast, CiscoGroupM, Accenture, Fifth Third Bank
Top Industries
VISITORS READING REVIEWS
Computer Software Company38%
Comms Service Provider11%
Media Company9%
Insurance Company7%
VISITORS READING REVIEWS
Computer Software Company37%
Comms Service Provider11%
Retailer8%
Media Company5%
Find out what your peers are saying about H2O.ai vs. IBM Watson Studio and other solutions. Updated: September 2020.
438,043 professionals have used our research since 2012.
H2O.ai is ranked 11th in Data Science Platforms with 5 reviews while IBM Watson Studio is ranked 9th in Data Science Platforms with 6 reviews. H2O.ai is rated 7.6, while IBM Watson Studio is rated 8.2. 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 IBM Watson Studio writes "Machine learning that can be applicable for other data sets without having to carry out the process all over again". H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and Cloudera Data Science Workbench, whereas IBM Watson Studio is most compared with Microsoft Azure Machine Learning Studio, IBM SPSS Modeler, Amazon SageMaker, Dataiku Data Science Studio and Databricks. See our H2O.ai vs. IBM Watson Studio report.

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