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."
"We've had no problems with SageMaker's stability."
"The deployment is very good, where you only need to press a few buttons."
"We were able to use the product to automate processes."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"The few projects we have done have been promising."
"ML Studio is very easy to maintain."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"The most valuable feature is data normalization."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics."
"It has helped in reducing the time involved for coding using R and/or Python."
"It's a great option if you are fairly new and don't want to write too much code."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"AI is a new area and AWS needs to have an internship training program available."
"Lacking in some machine learning pipelines."
"The solution is complex to use."
"The solution needs to be cheaper since it now charges per document for extraction."
"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."
"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."
"Integration with social media would be a valuable enhancement."
"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."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"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."
"The platform's integration feature could be better."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
"The solution should be more customizable. There should be more algorithms."
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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