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."
"Allows you to create API endpoints."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"We've had no problems with SageMaker's stability."
"They are doing a good job of evolving."
"The few projects we have done have been promising."
"IBM Watson Studio consistently automates across channels."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"Stability-wise, it is a great tool."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It has a lot of data connectors, which is extremely helpful."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"The solution is very easy to use."
"It is a stable, reliable product."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"AI is a new area and AWS needs to have an internship training program available."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"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 needs to be cheaper since it now charges per document for extraction."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"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 documentation must be made clearer and more user-friendly."
"I want IBM's technical support team to provide more specific answers to queries."
"The decision making in their decision making feature is less good than other options."
"So a better user interface could be very helpful"
"The solution's interface is very slow at times."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"The initial setup was complex."
"We would like to see it more web-based with more functionality."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. Amazon SageMaker is rated 7.2, 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, Microsoft Azure Machine Learning Studio, Azure OpenAI, Google Vertex AI and Amazon Comprehend. See our Amazon SageMaker vs. IBM Watson Studio report.
See our list of best Data Science Platforms vendors and best AI Development Platforms vendors.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
