We performed a comparison between Amazon SageMaker and IBM Watson Studio 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 deployment is very good, where you only need to press a few buttons."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"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."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"We were able to use the product to automate processes."
"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."
"We've had no problems with SageMaker's stability."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"It is a very stable and reliable solution."
"The solution is very easy to use."
"It is a stable, reliable product."
"The system's ability to take a look at data, segment it and then use that data very differently."
"Stability-wise, it is a great tool."
"It has a lot of data connectors, which is extremely helpful."
"It has greatly improved the performance because it is standardized across the company."
"The scalability of IBM Watson Studio is great."
"SageMaker would be improved with the addition of reporting services."
"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 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."
"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 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."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"I want IBM's technical support team to provide more specific answers to queries."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"We would like to see it more web-based with more functionality."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while IBM Watson Studio is ranked 10th in Data Science Platforms with 13 reviews. Amazon SageMaker is rated 7.4, while IBM Watson Studio is rated 8.2. 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 IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas IBM Watson Studio is most compared with Databricks, Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI and Amazon Comprehend. See our Amazon SageMaker vs. IBM Watson Studio report.
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