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."They are doing a good job of evolving."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"We've had no problems with SageMaker's stability."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The deployment is very good, where you only need to press a few buttons."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"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 that you don't have to do any programming in order to perform some of your use cases."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The solution is complex 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."
"There are other better solutions for large data, such as Databricks."
"The documentation must be made clearer and more user-friendly."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"Lacking in some machine learning pipelines."
"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 18 reviews while DataRobot is ranked 12th in AI Development Platforms. Amazon SageMaker is rated 7.2, 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, SAS Predictive Analytics and Alteryx.
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