We performed a comparison between Amazon SageMaker and Google Cloud Datalab based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."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 few projects we have done have been promising."
"They are doing a good job of evolving."
"Allows you to create API endpoints."
"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 superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"Google Cloud Datalab is very customizable."
"All of the features of this product are quite good."
"The APIs are valuable."
"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 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."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The documentation must be made clearer and more user-friendly."
"The solution is complex to use."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"There are other better solutions for large data, such as Databricks."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The product must be made more user-friendly."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"The interface should be more user-friendly."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while Google Cloud Datalab is ranked 14th in Data Science Platforms with 5 reviews. Amazon SageMaker is rated 7.2, while Google Cloud Datalab is rated 7.6. 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 Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, IBM SPSS Modeler and KNIME. See our Amazon SageMaker vs. Google Cloud Datalab report.
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