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."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 most valuable feature of Amazon SageMaker for me is the model deployment service."
"We were able to use the product to automate processes."
"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."
"They are doing a good job of evolving."
"The deployment is very good, where you only need to press a few buttons."
"The few projects we have done have been promising."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The solution needs to be cheaper since it now charges per document for extraction."
"The solution requires a lot of data to train the model."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"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."
"Lacking in some machine learning pipelines."
"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."
More Cloudera Data Science Workbench Pricing and Cost Advice →
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.
See our list of best Data Science Platforms vendors.
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.
