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 most valuable feature of Amazon SageMaker for me is the model deployment service."
"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."
"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 solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"We were able to use the product to automate processes."
"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 solution needs to be cheaper since it now charges per document for extraction."
"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 product must provide better documentation."
"The solution is complex to use."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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."
"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 solution requires a lot of data to train the model."
"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 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.
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