Amazon SageMaker vs Data Science Workbench

Likelihood to Recommend

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.

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Thomas Young

Owner, previous CEO

Econometric StudiosFinancial Services, 11-50 employees

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If you already have a Cloudera partnership and a cluster, having this is a no brainer.
It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Feature Rating Comparison

Amazon SageMaker

Data Science Workbench

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

MDM Integration

Amazon SageMaker

Data Science Workbench

Visualization

Interactive Data Analysis

Amazon SageMaker

Data Science Workbench

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Amazon SageMaker

Data Science Workbench

Multiple Model Development Languages and Tools

Single platform for multiple model development

Self-Service Model Delivery

Amazon SageMaker

Data Science Workbench

Flexible Model Publishing Options

Security, Governance, and Cost Controls

Pros

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.

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Verified User

Employee in Human Resources

Real Estate Company, 1001-5000 employees

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The ability to use multiple languages.
GitHub integration.
Scalable.

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Verified User

Professional in Research & Development

Utilities Company, 10,001+ employees

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Cons

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.

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Verified User

Employee in Research & Development

Computer Software Company, 501-1000 employees

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Not as great as RStudio; lacks some features when compared with it
It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Support Rating

No score

No answers yet

No answers on this topic

Data Science Workbench 5.0

Based on 1 answer

It is expensive and difficult to install and maintain.

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Verified User

Professional in Research & Development

Utilities Company, 10,001+ employees

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Alternatives Considered

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.

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Verified User

Professional in Legal

Legal Services Company, 51-200 employees

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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.

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Return on Investment

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.

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Gavin Hackeling

Data Scientist

Wonder (AskWonder.com)Research, 11-50 employees

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Paid off for demonstration purposes.

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Verified User

Professional in Research & Development

Utilities Company, 10,001+ employees

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Pricing Details

General

Free Trial

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Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

General

Free Trial

—

Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Rating Summary

Amazon SageMaker

Data Science Workbench

Amazon SageMaker

Data Science Workbench

Likelihood to Recommend