Compare Cloudera Data Science Workbench vs. H2O.ai

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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"The Cloudera Data Science Workbench is customizable and easy to use."

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"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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Cons
"Running this solution requires a minimum of 12GB to 16GB of RAM."

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"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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Questions from the Community
Top Answer: The Cloudera Data Science Workbench is customizable and easy to use.
Top Answer: Running this solution requires a minimum of 12GB to 16GB of RAM. In the future, I would like to see a student version of the Data Science Workbench that includes sample datasets that can be used for… more »
Top Answer: I am a professor and this is one of the solutions that I use as a teaching tool for my students. The most recent version can be used by the students while they are working in the labs because our… more »
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Ranking
18th
Views
4,112
Comparisons
3,615
Reviews
1
Average Words per Review
302
Rating
8.0
14th
Views
7,649
Comparisons
5,062
Reviews
1
Average Words per Review
475
Rating
7.0
Popular Comparisons
Also Known As
CDSW
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Overview

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.

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.

Offer
Learn more about Cloudera Data Science Workbench
Learn more about H2O.ai
Sample Customers
IQVIA, Rush University Medical Center, Western Union
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Top Industries
VISITORS READING REVIEWS
Computer Software Company30%
Comms Service Provider14%
Financial Services Firm13%
Insurance Company6%
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider15%
Financial Services Firm8%
Media Company6%
Company Size
No Data Available
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: March 2021.
475,705 professionals have used our research since 2012.

Cloudera Data Science Workbench is ranked 18th in Data Science Platforms with 1 review while H2O.ai is ranked 14th in Data Science Platforms with 1 review. Cloudera Data Science Workbench is rated 8.0, while H2O.ai is rated 7.0. The top reviewer of Cloudera Data Science Workbench writes "Customizable, easy to install, and easy to use". On the other hand, the top reviewer of H2O.ai writes "Good collaboration functionality, but better integration with Python for data science is needed". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Alteryx, Anaconda and Google Cloud Datalab, whereas H2O.ai is most compared with KNIME, Amazon SageMaker, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Anaconda.

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