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 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."
"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."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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 deployment is very good, where you only need to press a few buttons."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"It has greatly improved the performance because it is standardized across the company."
"It has a lot of data connectors, which is extremely helpful."
"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."
"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 stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"IBM Watson Studio consistently automates across channels."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"Lacking in some machine learning pipelines."
"There are other better solutions for large data, such as Databricks."
"AI is a new area and AWS needs to have an internship training program available."
"The documentation must be made clearer and more user-friendly."
"The solution is complex to use."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"I think maybe the support is an area where it lacks."
"So a better user interface could be very helpful"
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"The solution's interface is very slow at times."
"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."
"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 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.
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.
