We performed a comparison between Amazon SageMaker and IBM Watson Machine Learning based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The few projects we have done have been promising."
"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."
"We were able to use the product to automate processes."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"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."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"It is has a lot of good features and we find the image classification very useful."
"I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive."
"The most valuable aspect of the solution's the cost and human labor savings."
"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."
"Scalability-wise, I rate the solution ten out of ten."
"It has improved self-service and customer satisfaction."
"The solution requires a lot of data to train the model."
"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 documentation must be made clearer and more user-friendly."
"AI is a new area and AWS needs to have an internship training program available."
"Lacking in some machine learning pipelines."
"SageMaker would be improved with the addition of reporting services."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The product must provide better documentation."
"If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use."
"In future releases, I would like to see a more flexible environment."
"The supporting language is limited."
"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."
"Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that."
"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while IBM Watson Machine Learning is ranked 9th in AI Development Platforms with 6 reviews. Amazon SageMaker is rated 7.4, while IBM Watson Machine Learning is rated 8.0. 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 Machine Learning writes "A highly efficient solution that delivers the desired results to its users". 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 Machine Learning is most compared with Google Cloud AI Platform, Azure OpenAI and TensorFlow. See our Amazon SageMaker vs. IBM Watson Machine Learning report.
See our list of best AI Development Platforms vendors.
We monitor all AI Development 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.
