Amazon SageMaker vs SAP Predictive Analytics

• 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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• It doesn't require you to have a Ph.D. to build models!
• You can use it to address a very large and wide dataset without worrying about sampling.
• Automation is in the product DNA. You can prepare your data, ingest it into the "Kernel", then get insights about what was found, decide to publish it and schedule scoring tasks or model refresh in the same product.

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• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

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• It's extremely hard to get started. We even have a data scientist who, when we put Predictive Analytics in front of them, they could not intuitively create a data model and start analyzing. Even with deep knowledge of data analysis, the interface isn't intuitive and it's hard to get to the point of having imported a data set and start analyzing/predicting
• Our platform team had confusion installing Predictive Analytics, particularly on the BI Platform. Additional complexity came when it came to using APL Libraries (AFL Wrapper or Stored Procedures)
• We are still working on merging datasets. In theory, we get how to do it but we come across all sorts of issues which don't occur in Spotfire (missing records etc.)

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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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• It will potentially save time in forecast creation by giving an algorithm-derived forecast which can then be adjusted vs. starting from scratch. We can call this, a system enabled forecast
• Thresholds can be created based on historical data to flag actuals as in/out of the 'norm', or to limit the scope of an investigation
• We spent a lot of time so far and haven't got very far with it - it's been quite frustrating to use and a lot of time/money has been invested

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