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 solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"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."
"We were able to use the product to automate processes."
"The deployment is very good, where you only need to press a few buttons."
"They are doing a good job of evolving."
"The few projects we have done have been promising."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"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."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The solution is very fast and simple for a data science solution."
"The initial setup is very simple and straightforward."
"The product's standout feature is a robust multi-file network with limited availability."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"Lacking in some machine learning pipelines."
"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."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"SageMaker would be improved with the addition of reporting services."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The solution requires a lot of data to train the model."
"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."
"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."
"Technical support could improve their turnaround time."
"The interface is a bit overloaded."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
"I would like to see modules to handle Deep Learning frameworks."
"The speed of deployment should be faster, as should testing."
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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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