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 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."
"We were able to use the product to automate processes."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The few projects we have done have been promising."
"Allows you to create API endpoints."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"We've had no problems with SageMaker's stability."
"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."
"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."
"The product must provide better documentation."
"Lacking in some machine learning pipelines."
"AI is a new area and AWS needs to have an internship training program available."
"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."
"SageMaker would be improved with the addition of reporting services."
"The documentation must be made clearer and more user-friendly."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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 18 reviews while Cloudera Data Science Workbench is ranked 17th in Data Science Platforms with 2 reviews. Amazon SageMaker is rated 7.2, 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.
