We performed a comparison between Amazon SageMaker and Cloudera Data Science Workbench based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."We were able to use the product to automate processes."
"We've had no problems with SageMaker's stability."
"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."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"Allows you to create API endpoints."
"They are doing a good job of evolving."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
"The documentation must be made clearer and more user-friendly."
"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The product must provide better documentation."
"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."
"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."
"The tool's MLOps is not good. It's pricing also needs to improve."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
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Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Cloudera Data Science Workbench is ranked 18th in Data Science Platforms with 2 reviews. Amazon SageMaker is rated 7.4, while Cloudera Data Science Workbench is rated 7.0. 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 Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Google Cloud AI Platform, whereas Cloudera Data Science Workbench is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, Google Cloud Datalab and Alteryx.
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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.
