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Forget the configuration. Use DSx.
March 06, 2018

Forget the configuration. Use DSx.

Facundo Ferrín | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with IBM Data Science Experience (DSx)

We use DSX in the development department. Previously, we spent a lot of time configuring our machines, setting up the computers and the virtual environments, getting the libraries. We can show our work to our boss without going with our computers. WIth DSX, we are able to do our work in the platform and share it with whoever we want. Then, they can leave comments in the notebook and we can review them later.
  • Configuration: You can forget about all the setup. You just open a notebook, import the libraries you want and start writing
  • Sharing: You can share your thoughts with anyone, because all your code lives in the cloud
  • Tools: IBM has amazing tools for speech recognition, image processing, and so on
  • Because of my use, I didn't find anything to improve. I think that making things more visual will be useful for non-expert people, like flowcharts for example
  • Time saving: As I said previously, I got a model in less than two weeks
I used Python and Jupyter on my personal computer, along with some commons libraries in the field, like scikit-learn, numpy and matplotlib. But, as I mentioned before, every time I wanted to share my work I just simply could not do it. Data Science Experience takes care of all the configuration part and leaves you ready to code.
IBM Cloud IaaS (formerly IBM Bluemix - IaaS), IBM Watson Analytics
I felt quite comfortable using DSX for prototyping. I was able to build an interesting model in less than two weeks, and I found it to be very productive to be able to share my ideas with the client and receive back their comments.

After that, I had to implement this model to be used as a REST service. I tried to do that with DSX but it was not possible, which is reasonable since it is not designed to do that.