We performed a comparison between Amazon SageMaker and MathWorks Matlab based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"They are doing a good job of evolving."
"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."
"Allows you to create API endpoints."
"We've had no problems with SageMaker's stability."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"Personally for me, because I do a lot of development, I like that it is easy to test mathematical algorithms with several matrix calculations. It's perfect for that."
"The tool's most valuable feature is the Simulink environment. This feature provides an incredible capability to visually represent the system behavior before creating the code. It allows you to see the flow and interactions of the system, which is extremely beneficial for software development. With this visual representation, you can better understand the system's behavior, make necessary adjustments, and ensure maintenance and updates. This capability is why I love working with the product."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"AI is a new area and AWS needs to have an internship training program available."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"SageMaker would be improved with the addition of reporting services."
"The solution requires a lot of data to train the model."
"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."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"In the area of improvement, sometimes there are issues with the speed of MathWorks Matlab, particularly in the Simulink environment. The tool's latest versions can be slow to open, taking significant time to load. Additionally, saving data and integrating models can also be time-consuming processes."
"To make use of the GPU, you have to have an Nvidia card. What I would want it to do is to run in Next Generation with Intel or AMD, and not just with Nvidia."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while MathWorks Matlab is ranked 14th in Data Science Platforms with 2 reviews. Amazon SageMaker is rated 7.4, while MathWorks Matlab is rated 8.6. 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 MathWorks Matlab writes "Has Simulink feature which helps with visual representations ". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas MathWorks Matlab is most compared with IBM SPSS Statistics, Databricks, Anaconda and Microsoft Azure Machine Learning Studio.
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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
