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."Allows you to create API endpoints."
"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."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The few projects we have done have been promising."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"It has greatly improved the performance because it is standardized across the company."
"It is a stable, reliable product."
"Stability-wise, it is a great tool."
"IBM Watson Studio consistently automates across channels."
"It has a lot of data connectors, which is extremely helpful."
"The scalability of IBM Watson Studio is great."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"Watson Studio is very stable."
"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."
"There are other better solutions for large data, such as Databricks."
"The product must provide better documentation."
"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."
"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 say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"SageMaker would be improved with the addition of reporting services."
"The documentation must be made clearer and more user-friendly."
"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."
"The initial setup was complex."
"The decision making in their decision making feature is less good than other options."
"The main challenge lies in visibility and ease of use."
"So a better user interface could be very helpful"
"I think maybe the support is an area where it lacks."
"The solution's interface is very slow at times."
"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."
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.
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.
