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 most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"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 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 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 product aggregates everything we need to build and deploy machine learning models in one place."
"The deployment is very good, where you only need to press a few buttons."
"The few projects we have done have been promising."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"IBM Watson Studio consistently automates across channels."
"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."
"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."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"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."
"It is a very stable and reliable solution."
"The solution needs to be cheaper since it now charges per document for extraction."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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."
"There are other better solutions for large data, such as Databricks."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"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 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."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"The initial setup was complex."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"The main challenge lies in visibility and ease of use."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"I want IBM's technical support team to provide more specific answers to queries."
"The solution's interface is very slow at times."
"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 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 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.