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 superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"Allows you to create API endpoints."
"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 Amazon SageMaker for me is the model deployment service."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"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 is data normalization."
"It's easy to use."
"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 ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"The product's standout feature is a robust multi-file network with limited availability."
"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."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"The solution requires a lot of data to train the model."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"The product must provide better documentation."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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."
"The solution needs to be cheaper since it now charges per document for extraction."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"SageMaker would be improved with the addition of reporting services."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"The solution should be more customizable. There should be more algorithms."
"The data cleaning functionality is something that could be better and needs to be improved."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"The regulatory requirements of the product need improvement."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 49 reviews. Amazon SageMaker is rated 7.2, 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 Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and IBM Watson Studio. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.
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