We performed a comparison between Amazon SageMaker and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The product aggregates everything we need to build and deploy machine learning models in one place."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"The deployment is very good, where you only need to press a few buttons."
"Allows you to create API endpoints."
"We've had no problems with SageMaker's stability."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"Their web interface is good."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet. The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"Lacking in some machine learning pipelines."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"AI is a new area and AWS needs to have an internship training program available."
"There are other better solutions for large data, such as Databricks."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"It could use to add some more features in data transformation, time series and the text analytics section."
"I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else."
"Microsoft should also include more examples and tutorials for using this product."
"The initial setup time of the containers to run the experiment is a bit long."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews. Amazon SageMaker is rated 7.4, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Google Cloud AI Platform, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and IBM SPSS Statistics. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.
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