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 most valuable feature of Amazon SageMaker for me is the model deployment service."
"The deployment is very good, where you only need to press a few buttons."
"We were able to use the product to automate processes."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The few projects we have done have been promising."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"Their web interface is good."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The product's standout feature is a robust multi-file network with limited availability."
"The solution is scalable."
"It is a scalable solution…It is a pretty stable solution…The solution's initial setup process was pretty straightforward."
"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."
"Regarding the technical support for the solution, I find the documentation provided comprehensive and helpful."
"Lacking in some machine learning pipelines."
"There are other better solutions for large data, such as Databricks."
"The documentation must be made clearer and more user-friendly."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The solution requires a lot of data to train the model."
"The solution is complex to use."
"It could use to add some more features in data transformation, time series and the text analytics section."
"There should be data access security, a role level security. Right now, they don't offer this."
"Integration with social media would be a valuable enhancement."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"The interface is a bit overloaded."
"I would like to see modules to handle Deep Learning frameworks."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"Microsoft should also include more examples and tutorials for using this product."
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 52 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 Dataiku, 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.
See our list of best Data Science Platforms vendors and best AI Development 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.