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's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"We were able to use the product to automate processes."
"We've had no problems with SageMaker's stability."
"The deployment is very good, where you only need to press a few buttons."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The few projects we have done have been promising."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"Their web interface is good."
"I find Microsoft Azure Machine Learning Studio advantageous because it allows integration with Titan Scratch and offers an easy-to-use drag-and-drop menu for developing machine learning models."
"Their support is helpful."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The initial setup is very simple and straightforward."
"It's a great option if you are fairly new and don't want to write too much code."
"It's easy to use."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"The solution requires a lot of data to train the 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."
"The solution is complex to use."
"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"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."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"The data cleaning functionality is something that could be better and needs to be improved."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"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."
"The solution cannot connect to private block storage."
"The product must improve its documentation."
"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."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
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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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