Creating your own data science workbench
In order to address common frictions for developing models in data science, as described in the previous section, we need to provide data scientists and practitioners with a standardized environment in which they can develop and manage their work. A data science workbench should allow you to quick-start a project, and the availability of an environment with a set of starting tools and frameworks allows data scientists to rapidly jump-start a project.
The data scientist and machine learning practitioner are at the center of the workbench: they should have a reliable platform that allows them to develop and add value to the organization, with their models at their fingertips.
The following diagram depicts the core features of a data science workbench:
Figure 3.1 – Core features of a data science workbench
In order to think about the design of our data science workbench and based on the diagram in Figure...
