Amazon SageMaker vs IBM SPSS Modeler

• Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
• Customization and easy to alter and change.
• If you already are an Amazon user, you do not need to transition over to another software.

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• GUI is really well accomplished and friendly, almost everyone with little investment in training can take advantage of the tool.
• Escalability, you can grow your investment in licensing according to your actual needs, from an annual authorized user, to perpetual concurrent and Big Data and Machine Learning capabilities.
• Open Sorce Ready: take leverage of all your developments made in R or Python and deployment all over the organization even with the user who isn´t used to code.

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• I think that although the algorithms are there and you are using one click, there could be more detailed descriptions located in places so that other users are able to easily find the right formula and tools.
• Mobile friendly options would be a huge plus, even tracking what employees are using this tool for in regards to reporting.

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• Some Analyses aren't there out of the box but can be added through open languages like R and Python.
• Graphs could be better.
• Unable to read data stored in OLAP databases

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• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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• Being able to start analytics oriented management and decision taking in organizations.
• Reduce times related to implementation and training in projects.
• A single platform can be used for all analytical process

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