What users are saying about
21 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener noreferrer'>trScore algorithm: Learn more.</a>Score 8.1 out of 100
Based on 21 reviews and ratings
14 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener noreferrer'>trScore algorithm: Learn more.</a>Score 7.5 out of 100
Based on 14 reviews and ratings
Likelihood to Recommend
Amazon SageMaker
Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
Owner, previous CEO
Econometric StudiosFinancial Services, 11-50 employees
Data Science Workbench
Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.

Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employeesFeature Rating Comparison
Platform Connectivity
Amazon SageMaker
—
Data Science Workbench
7.3
Connect to Multiple Data Sources
Amazon SageMaker
—
Data Science Workbench
6.7
Extend Existing Data Sources
Amazon SageMaker
—
Data Science Workbench
7.7
Automatic Data Format Detection
Amazon SageMaker
—
Data Science Workbench
7.0
MDM Integration
Amazon SageMaker
—
Data Science Workbench
8.0
Data Exploration
Amazon SageMaker
—
Data Science Workbench
8.0
Visualization
Amazon SageMaker
—
Data Science Workbench
7.6
Interactive Data Analysis
Amazon SageMaker
—
Data Science Workbench
8.3
Data Preparation
Amazon SageMaker
—
Data Science Workbench
7.8
Interactive Data Cleaning and Enrichment
Amazon SageMaker
—
Data Science Workbench
7.3
Data Transformations
Amazon SageMaker
—
Data Science Workbench
8.0
Data Encryption
Amazon SageMaker
—
Data Science Workbench
8.0
Built-in Processors
Amazon SageMaker
—
Data Science Workbench
7.7
Platform Data Modeling
Amazon SageMaker
—
Data Science Workbench
8.0
Multiple Model Development Languages and Tools
Amazon SageMaker
—
Data Science Workbench
8.3
Automated Machine Learning
Amazon SageMaker
—
Data Science Workbench
7.0
Single platform for multiple model development
Amazon SageMaker
—
Data Science Workbench
7.9
Self-Service Model Delivery
Amazon SageMaker
—
Data Science Workbench
8.6
Model Deployment
Amazon SageMaker
—
Data Science Workbench
7.7
Flexible Model Publishing Options
Amazon SageMaker
—
Data Science Workbench
8.6
Security, Governance, and Cost Controls
Amazon SageMaker
—
Data Science Workbench
6.8
Pros
Amazon SageMaker
- Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
- Customization and easy to alter and change.
- If you already are an Amazon user, you do not need to transition over to another software.

Verified User
Employee in Human Resources
Real Estate Company, 1001-5000 employeesData Science Workbench
- One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
- For larger organizations/teams, it lets you be self reliant
- As it sits on your cluster, it has very easy access of all the data on the HDFS
- Linking with Github is a very good way to keep the code versions intact
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.Retail, 10,001+ employees
Cons
Amazon SageMaker
- It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
- Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Verified User
Employee in Research & Development
Computer Software Company, 501-1000 employeesData Science Workbench
- Installation is difficult.
- Upgrades are difficult.
- Licensing options are not flexible.

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employeesSupport Rating
Amazon SageMaker
No score
No answers yet
No answers on this topic
Data Science Workbench
Data Science Workbench 7.1
Based on 2 answers
Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.

Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employeesAlternatives Considered
Amazon SageMaker
Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

Verified User
Professional in Legal
Legal Services Company, 51-200 employeesData Science Workbench
Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.Retail, 10,001+ employees
Return on Investment
Amazon SageMaker
- We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
- We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
- For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
Data Scientist
Wonder (AskWonder.com)Research, 11-50 employees
Data Science Workbench
- Paid off for demonstration purposes.

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employeesPricing Details
Amazon SageMaker
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Data Science Workbench
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No