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."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."
"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 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 most valuable feature of Amazon SageMaker for me is the model deployment service."
"We were able to use the product to automate processes."
"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 scalability of IBM Watson Studio is great."
"It has a lot of data connectors, which is extremely helpful."
"Watson Studio is very stable."
"The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video."
"IBM Watson Studio consistently automates across channels."
"The system's ability to take a look at data, segment it and then use that data very differently."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"The solution is very easy to use."
"There are other better solutions for large data, such as Databricks."
"The product must provide better documentation."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"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."
"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."
"Lacking in some machine learning pipelines."
"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."
"The decision making in their decision making feature is less good than other options."
"I think maybe the support is an area where it lacks."
"I want IBM's technical support team to provide more specific answers to queries."
"The solution's interface is very slow at times."
"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."
"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."
"The main challenge lies in visibility and ease of use."
"So a better user interface could be very helpful"
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while IBM Watson Studio is ranked 11th 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 Dataiku, 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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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.
