Amazon Tensor Flow vs RStudio

• Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
• Tensor Flow allows us to train our models much faster than in our on-premise equipment.
• Most of the pre-trained models are easy to adapt to our clients' needs.

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• Excellent integration of both R and Python IDEs in one.
• Simple publishing of dashboards and applications from RStudio IDE to RStudio Connect.
• Integration of package management with projects to support collaboration.
• Excellent contributors to the R Open Source community, really invested in its health.
• Support integration with Enterprise AD environments for security.

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• SageMaker isn't available in all regions. This is complicated for some clients overseas.
• For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
• Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.

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• Management of some deeper aspects of the platform is not a so straight-forward, especially when it comes to deal to customization (connections, packages management...).
• Administration console may be a bit richer, making available of some operations that you may be interested on doing by user interface and not by shell.
• Deploying apps is still a bit problematic for some particular (rare!) packages, make it easier to install packages not from the CRAN.

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• Positive: It has allowed us to work with our overseas teams without any large hardware investing.
• Positive: Pre-trained models significantly reduce the time to develop solutions for our clients.
• Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.

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• RStudio has allowed us to rapidly build and deploy functional Shiny applications that execute underlying code. In many cases, users don't realize they are running R under the hood.
• Allowed us to integrate Python code and R code in a single application/visualization.

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