Amazon Deep Learning AMIs vs IBM Watson Studio

• You can get several common packages including keras, pytorch and tensorflow setup within an environment ready to code on any AWS instance which saves time
• Great for virtual applications that helps communicate between various pieces of software
• Not need to worry about compatibility or any major aspects of setup e.g. GPU configuration

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• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

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• Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
• It can be a bit confusing to switch between frameworks for novice users

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• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

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• It has made our Data Science/ Machine Learning Courses easier to manage/ need less human input therefore allowing us to increase the cohort size for this degree
• It has unified a lot of technologies reducing the load on our IT team

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• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

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