We performed a comparison between Amazon SageMaker and DataRobot based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Google, TensorFlow and others in AI Development Platforms."The few projects we have done have been promising."
"We were able to use the product to automate processes."
"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 solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The deployment is very good, where you only need to press a few buttons."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"DataRobot can be easy to use."
"There are other better solutions for large data, such as Databricks."
"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."
"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."
"Lacking in some machine learning pipelines."
"SageMaker would be improved with the addition of reporting services."
"The solution needs to be cheaper since it now charges per document for extraction."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
Earn 20 points
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while DataRobot is ranked 13th in AI Development Platforms. Amazon SageMaker is rated 7.4, while DataRobot is rated 8.0. 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 DataRobot writes "Easy to use, priced well, and can be customized". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Cloudera Data Science Workbench, whereas DataRobot is most compared with RapidMiner, Microsoft Azure Machine Learning Studio, Datadog, Alteryx and SAS Predictive Analytics.
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