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 few projects we have done have been promising."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The deployment is very good, where you only need to press a few buttons."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"They are doing a good job of evolving."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"Their web interface is good."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
"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."
"It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"Lacking in some machine learning pipelines."
"The documentation must be made clearer and more user-friendly."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The product must provide better documentation."
"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."
"They should have a desktop version to work on the platform."
"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."
"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."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"The solution's initial setup process is complicated."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
"There's room for improvement in terms of binding the integration with Azure DevOps."
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 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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