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."Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"They are doing a good job of evolving."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"We've had no problems with SageMaker's stability."
"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."
"It has greatly improved the performance because it is standardized across the company."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"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."
"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."
"It is a stable, reliable 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."
"There are other better solutions for large data, such as Databricks."
"The solution requires a lot of data to train the model."
"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."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"Lacking in some machine learning pipelines."
"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."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"I think maybe the support is an area where it lacks."
"The main challenge lies in visibility and ease of use."
"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."
"The solution's interface is very slow at times."
"The decision making in their decision making feature is less good than other options."
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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