IBM Watson Studio on Cloud Pak for Data vs. Wolfram Mathematica

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

Incentivized

We are the judgement that Wolfram Mathematica is despite many critics based on the paradigms selected a mark in the fields of the markets for computations of all kind. Wolfram Mathematica is even a choice in fields where other bolide systems reign most of the market. Wolfram Mathematica offers rich flexibility and internally standardizes the right methodologies for his user community. Wolfram Mathematica is not cheap and in need of a hard an long learner journey. That makes it weak in comparison with of-the-shelf-solution packages or even other programming languages. But for systematization of methods Wolfram Mathematica is far in front of almost all the other. Scientist and interested people are able to develop themself further and Wolfram Matheamatica users are a human variant for themself. The reach out for modern mathematics based science is deep and a unique unified framework makes the whole field of mathematics accessable comparable to the brain of Albert Einstein. The paradigms incorporated are the most efficients and consist in assembly on the market. The mathematics is covering and fullfills not just education requirements but the demands and needs of experts.

Mathematica is incompatible with other systems for mCAx and therefore the borders between the systems are hard to overcome. Wolfram Mathematica should be consider one of the more open systems because other code can be imported and run but on the export side it is rathe incompatible by design purposes. A better standard for all that might solve the crisis but there is none in sight. Selection of knowledge of what works will be in the future even more focussed and general system might be one the lossy side. Knowledge of esthetics of what will be in the highest demand in necessary and Wolfram is not a leader in this field of science. Mathematics leves from gathering problems from application fields and less from the glory of itself and the formalization of this.

• 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.

Incentivized

• Doing the analysis is very good, plotting the data, getting the insight from data using this is easy and output looks pretty good
• Solving mathematical problems, with detailed solutions step by step (mainly calculus problems)
• You can also query this knowledge engine in simple English. It has ability to interpret that and will give you answer accordingly
• Nowadays, it provides API to use, so you can do image processing, video/audio using this

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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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• Should include more libraries and functions.
• Should include more functions that can be used in Machine Learning.
• Should include more functions that can be used in Data Science.

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because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

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The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

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From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

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Never had slow response even on our very busy network

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I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

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Wolfram Mathematica is a nice software package. It has very nice features and easy to install and use in your machine. Besides this, there is a nice support from Wolfram. They come to the university frequently to give seminars in Mathematica. I think this is the best thing they are doing. That is very helpful for graduate and undergraduate students who are using Mathematica in their research.

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The trainers on the job are very smart with solutions and very able in teaching

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The Platform is very handy and suggests further steps according my previous interests

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It surprised us with unpredictable case of use and brand new points of view

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The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

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The ability to manipulate algebraic expressions, nested lists, and data structures in Mathematica was unequalled when I first did the comparison. Since then, I've stuck with Mathematica mostly because it's "the tool I know."

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It helped us in getting from 0 to DSX without getting lost

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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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• Mathematica is our "go to" environment for developing solutions for our clients, so I suppose you could say that it is solely responsible for our revenues. On occasion we do use other platforms but Mathematica is a core component of our offer to clients.

Incentivized