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."They are doing a good job of evolving."
"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 tool makes our ML model development a bit more efficient because everything is in one environment."
"The few projects we have done have been promising."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"Allows you to create API endpoints."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"The solution is really scalable."
"The most valuable feature is data normalization."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"The solution requires a lot of data to train the model."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Lacking in some machine learning pipelines."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The product must provide better documentation."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"There should be data access security, a role level security. Right now, they don't offer this."
"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."
"Operability with R could be improved."
"Using the solution requires some specific learning which can take some time."
"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."
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 50 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 Data Science Studio, 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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