Amazon SageMaker vs Azure Machine Learning Studio

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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Well suited:- Run a machine learning model the fastest and easiest way;- Working with an organization with no coding background;- Trying to get the most of data the cheapest and easiest way possible;- Introducing analytics and machine learning concepts to an organization or class;Less appropriate:- Running complex Machine Learning models;- Visualizing data more deeply;- Running new analytics models;- Running heavy statistical models;

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Gabriel Chiararia

MBA Candidate | Class of 2018

Brigham Young University Marriott School of BusinessConsumer Electronics, 201-500 employees

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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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Adding python scripts
Pre-trained models
Case studies of industry projects

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Verified User

Professional in Information Technology

Education Management Company, 1001-5000 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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Hard to apply Python code and run
More models could be available
Tableau interface would be perfect

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Verified User

Team Lead in Information Technology

Machinery Company, 10,001+ employees

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Usability

No score

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Azure Machine Learning Studio 7.0

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Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.

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Verified User

Professional in Information Technology

Education Management Company, 1001-5000 employees

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Support

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Azure Machine Learning Studio 1.0

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Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.

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Verified User

Professional in Information Technology

Education Management Company, 1001-5000 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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The answer is quite simple: Microsoft Azure Machine Learning Workbench is the cheapest and most user friendly analytics tool I have ever seen! Unless you are running a team of data scientists, this is the tool to go. Most functions (marketing, sales, finance, supply chain, logistics, HR, R&D, etc.) could easily integrate Azure ML in its day to day activity.

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Gabriel Chiararia

MBA Candidate | Class of 2018

Brigham Young University Marriott School of BusinessConsumer Electronics, 201-500 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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