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Ability to use an integrated full featured IDE like code-server for complex data wrangling, framework development and modularization of programs
Many data science code development tasks are analytical and can be done through the notebooks available.
There are however tasks that require more sophisticated code develpment for which notebooks are not as suitable.
For these it would be benef...
Default RStudio environment definition should be increased to 2 vCPU and 4 GB RAM.
As Data Scientist we are working a lot with RStudio. The default RStudio environment definition (1 vCPU and 1 GB RAM) is too small for RStudio. The limit of 1 GB is quickly reached and the pod is then restarted. And you do not understand what happ...
Python packages installed by the Data Scientist are not persistently stored in the WSUSER home directory like in RStudio
When a runtime environment is stopped, the installed Python packages are also deleted. And you have to install the packages again when starting the runtime environment or after starting by Python program. This can take a long time for some package...
Show meaningful error messages (when there is no access to platform connections)
Currently notebooks can not be started, when there is a storage volume from platform connections in the project and the user does not have access to platform connection The error message shown is "Notebook could not be loaded", which does not prov...
Evolve Data Refinement to more advanced capabilities
To make Data Refinement more attractive to Data Scientists, more advanced capabilities should be available, like some of the features available in AutoAI today, but still with the opportunity to build models manually. Like Train/Test Split, Encodi...
If the Github repo associated with a project has been pulled to a local machine, and the constituent notebooks edited, there is no native way to pull the changes into the IBM Watson Studio project. This can hamper greatly appreciate collaboration ...
Resources are always an issue in Cloud Pak for Data, especially in small environments with only 48 cores. In installations that support “native” Data Science use cases (that is customers, just using Jupyter notebooks in WSL, doing their stuff in P...
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