Anaconda vs IBM Watson Studio

• Anaconda (i.e. Python with lots of packages and the fabulous iPython/Jupyter Notebook) does analytics well. In analytics, or "data science" or whatever buzzword, you have to pick your poison: Python, R, or SAS. Python is the only one that's good at doing other things as well.
• Like visualization...The quality of the built in types of scientific visualization in Python vs. R and their aesthetics is up for grabs. However, Python can do a whole lot of different kinds of visualization above and beyond R. Similarly, JavaScript probably can do more/better visualization than Python, but it's not meant for analytics. Anaconda has enough visualization packages to get you started.

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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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• It's hard to get security updates when you leave the system packages
• There're some PyPI packages that Anaconda doesn't have. This obligates the user to package it by herself or using pip
• Anaconda isn't as fast as PyPI publications

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• No ability to audit/log activity
• Difficult to secure
• No access to underlying infrastructure
• Poor reliability and performance
• Machine learning capability too basic/simple and black box nature means it is difficult to validate/trust
• Limited ability to customise Watson Analytics visualisation
• Poor support response times
• Poor native support for Softlayer S3 storage

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• We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
• We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.

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• IBM DSx helped us meet some needs of our business clients
• Not all business users could fully utilize DSx as they don't have much experience in coding
• Most Business requirements for a problem were solved

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