We performed a comparison between Amazon SageMaker and Cloudera Data Science Workbench 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."They are doing a good job of evolving."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"Allows you to create API endpoints."
"We've had no problems with SageMaker's stability."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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 Cloudera Data Science Workbench is customizable and easy to use."
"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."
"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."
"There are other better solutions for large data, such as Databricks."
"SageMaker would be improved with the addition of reporting services."
"The documentation must be made clearer and more user-friendly."
"The solution is complex to use."
"The solution needs to be cheaper since it now charges per document for extraction."
"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."
"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."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
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Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Cloudera Data Science Workbench is ranked 19th 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 KNIME, whereas Cloudera Data Science Workbench is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku, Google Cloud Datalab and Alteryx. See our Amazon SageMaker vs. Cloudera Data Science Workbench report.
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