Amazon SageMaker vs IBM SPSS Modeler

• SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
• SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
• SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.

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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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• SageMaker does not allow you to schedule training jobs.
• SageMaker does not provide a mechanism for easily tracking metrics logged during training.
• We often fit feature extraction and model pipelines. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors. We could provide our own container to SageMaker instead, but this is tantamount to serving the model ourselves.

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• Too much foreign programming software

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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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• Efficient result
• Validity of result
• Improved customer service

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