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 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 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 deployment is very good, where you only need to press a few buttons."
"We've had no problems with SageMaker's stability."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"Allows you to create API endpoints."
"All of the features of this product are quite good."
"Google Cloud Datalab is very customizable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"The APIs are valuable."
"The documentation must be made clearer and more user-friendly."
"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."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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 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."
"The solution requires a lot of data to train the model."
"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."
"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."
"The interface should be more user-friendly."
"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."
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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