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."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."
"They are doing a good job of evolving."
"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."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The solution is very fast and simple for a data science solution."
"The UI is very user-friendly and that AI is easy to use."
"Azure's AutoML feature is probably better than the competition."
"The product's standout feature is a robust multi-file network with limited availability."
"The most valuable feature is data normalization."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"Visualisation, and the possibility of sharing functions are key features."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The documentation must be made clearer and more user-friendly."
"The solution is complex to use."
"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."
"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 pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"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 should be more customizable. There should be more algorithms."
"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."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"It would be nice if the product offered more accessibility in general."
"The platform's integration feature could be better."
"The data cleaning functionality is something that could be better and needs to be improved."
"They should have a desktop version to work on the platform."
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 51 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 Watson Studio. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.
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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.