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Amazon SageMaker vs Cloudera Data Science Workbench comparison

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621,703 professionals have used our research since 2012.
Questions from the Community
Top Answer:We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It… more »
Top Answer:Hi @PankajUrmaliya and @reviewer1318050, Can you possibly assist @Larry Desjardins ​in answering their question? Thanks.
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 »
Ranking
9th
Views
13,036
Comparisons
10,496
Reviews
1
Average Words per Review
564
Rating
7.0
16th
Views
3,638
Comparisons
3,002
Reviews
0
Average Words per Review
0
Rating
N/A
Comparisons
Also Known As
AWS SageMaker, SageMaker
CDSW
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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.

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.

Offer
Learn more about Amazon SageMaker
Learn more about Cloudera Data Science Workbench
Sample Customers
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
IQVIA, Rush University Medical Center, Western Union
Top Industries
VISITORS READING REVIEWS
Computer Software Company21%
Comms Service Provider12%
Financial Services Firm10%
Media Company10%
VISITORS READING REVIEWS
Computer Software Company21%
Financial Services Firm21%
Comms Service Provider12%
Energy/Utilities Company6%
Company Size
REVIEWERS
Midsize Enterprise57%
Large Enterprise43%
VISITORS READING REVIEWS
Small Business14%
Midsize Enterprise11%
Large Enterprise75%
VISITORS READING REVIEWS
Small Business10%
Midsize Enterprise14%
Large Enterprise76%
Buyer's Guide
Data Science Platforms
July 2022
Find out what your peers are saying about Databricks, Alteryx, Microsoft and others in Data Science Platforms. Updated: July 2022.
621,703 professionals have used our research since 2012.

Amazon SageMaker is ranked 9th in Data Science Platforms with 1 review while Cloudera Data Science Workbench is ranked 16th in Data Science Platforms. Amazon SageMaker is rated 7.0, while Cloudera Data Science Workbench is rated 0.0. The top reviewer of Amazon SageMaker writes "Good deployment and monitoring features, but the interface could use some improvement". On the other hand, Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, Domino Data Science Platform and IBM SPSS Modeler, whereas Cloudera Data Science Workbench is most compared with Databricks, Microsoft Azure Machine Learning Studio, Anaconda, Dataiku Data Science Studio and Google Cloud Datalab.

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.