We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The deployment is very good, where you only need to press a few buttons."
"Allows you to create API endpoints."
"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."
"They are doing a good job of evolving."
"Very good data aggregation."
"Automation is great and this product is very organized."
"The supervised models are valuable. It is also very organized and easy to use."
"You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after."
"It is a great product for running statistical analysis."
"AI is a new area and AWS needs to have an internship training program available."
"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."
"Lacking in some machine learning pipelines."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"Requires more development."
"When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing."
"It would be good if IBM added help resources to the interface."
"Dimension reduction should be classified separately."
"The support costs are 10% of the Amazon fees and it comes by default."
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"This tool, being an IBM product, is pretty expensive."
"Its price is okay for a company, but for personal use, it is considered somewhat expensive."
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.
Amazon SageMaker is ranked 11th in Data Science Platforms with 4 reviews while IBM SPSS Modeler is ranked 8th in Data Science Platforms with 5 reviews. Amazon SageMaker is rated 8.0, while IBM SPSS Modeler is rated 8.4. The top reviewer of Amazon SageMaker writes "A solution with great computational storage, has many pre-built models, is stable, and has good support". On the other hand, the top reviewer of IBM SPSS Modeler writes "User-friendly, and it gives you a lot of visibility through features like comparing fiscal quarters". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Domino Data Science Platform, whereas IBM SPSS Modeler is most compared with IBM Watson Studio, KNIME, IBM SPSS Statistics, Alteryx and MathWorks Matlab. See our Amazon SageMaker vs. IBM SPSS Modeler report.
See our list of best Data Science 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.