Anonymous UserVice President & CIO at a logistics company
EzzAbdelfattahAssociate Professor of Statistics at KAU
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The few projects we have done have been promising."
"They are doing a good job of evolving."
"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."
"Allows you to create API endpoints."
"The deployment is very good, where you only need to press a few buttons."
"Most of the product features are good but I particularly like the linear regression analysis."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."
"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"It has the ability to easily change any variable in our research."
"The most valuable feature is the user interface because you don't need to write code."
"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"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."
"I think the visualization and charting should be changed and made easier and more effective."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"The statistics should be more self-explanatory with detailed automated reports."
"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"The design of the experience can be improved."
"This solution is not suitable for use with Big Data."
"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."
"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."
"The support costs are 10% of the Amazon fees and it comes by default."
"We think that IBM SPSS is expensive for this function."
"The price of this solution is a little bit high, which was a problem for my company."
"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."
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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.
Amazon SageMaker is ranked 13th in Data Science Platforms with 5 reviews while IBM SPSS Statistics is ranked 5th in Data Science Platforms with 15 reviews. Amazon SageMaker is rated 7.6, while IBM SPSS Statistics is rated 8.0. 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 Statistics writes "Offers good Bayesian and descriptive statistics". Amazon SageMaker is most compared with Databricks, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio, H2O.ai and Domino Data Science Platform, whereas IBM SPSS Statistics is most compared with IBM SPSS Modeler, TIBCO Statistica, MathWorks Matlab, Weka and Alteryx. See our Amazon SageMaker vs. IBM SPSS Statistics report.
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