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 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 most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"We were able to use the product to automate processes."
"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 for me is the model deployment service."
"Allows you to create API endpoints."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"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."
"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 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."
"It has a lot of data connectors, which is extremely helpful."
"Stability-wise, it is a great tool."
"It is a very stable and reliable solution."
"Lacking in some machine learning pipelines."
"The product must provide better documentation."
"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The solution needs to be cheaper since it now charges per document for extraction."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The solution requires a lot of data to train the model."
"SageMaker would be improved with the addition of reporting services."
"I think maybe the support is an area where it lacks."
"The solution's interface is very slow at times."
"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."
"I want IBM's technical support team to provide more specific answers to queries."
"The decision making in their decision making feature is less good than other options."
"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."
"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."
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.
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.