Suitability of DSx for a Data Science Project
March 14, 2018

Suitability of DSx for a Data Science Project

Anonymous | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Overall Satisfaction with IBM Data Science Experience (DSx)

We used the DSx platform in the context of a data science project in the medical domain. The general problem was to predict the health condition of a patient in real-time for the upcoming minutes based on various features that were provided by the customer. The status of a patient could be described by a limited number of classes which allowed us to interpret it as a classification problem.

Due to confidentiality reasons, we had to perform all tasks on the DSx. This included the analysis of the data set, the computation of additional features, the development and optimization of a machine learning model, as well as the analysis of the results. Therefore, we relied in particular on Jypiter notebooks (python and R) and RStudio

  • Standard software packages (python and R) are available and ready to run.
  • Data from various sources (e.g. external databases) accessed and loaded from DSx.
  • Customer support provides valuable guidance and helps to solve problems.
  • An actual IDE for python would be very helpful.
  • Some python packages were not up-to-date and it was not possible to install the current version.
  • It should be somehow possible to monitor the used resources and system load (CPU/RAM).
  • Fewer maintenance costs.
  • The system can be scaled up if necessary.
  • Platform availability is very good.
We did not evaluate comparable platforms. The customer suggested using DSx.
The DSx platform is an appropriate choice if a project cannot be carried out on on-premise systems. It provides standard data science software packages that are directly ready-to-use. However, the DSx imposes also some limitations that could be an issue for some projects. For instance, it might not be possible to install required non-standard software.