The typical data scientist consistently has a series of extremely complicated problems on their mind beyond considerations stemming from their system infrastructure. Still, it is inevitable that infrastructure issues will present themselves. To oversimplify, we might draw a distinction between the “modeling problem” and the “engineering problem.” The data scientist is uniquely qualified to solve the former, but can often come up short in solving the latter.
Docker has been widely adopted by the system administrator and DevOps community as a modern solution to the challenges presented in ...
