I finally got a working prototype of my journal recommender project up and running; I just shared it with a few co-workers yesterday. However, it has been a bit of a journey to get here: I was only able to get the project finished by backing away from Google Cloud Platform. I won’t dwell on my complaints with GCP; instead I want to focus how I got out of my predicament.

Basically, I solved the problems that were dogging the recommender project by returning as much as possible to python. One of the reasons that I found Google Cloud Platform so frustrating was because I had to do a lot of work outside of python.

The solution was to run the project on PythonAnywhere instead. The reason PA worked for me was that I could solve my problems directly in python, in the shell. For the most part, there were no additional layers of complexity. GCP is a much more comprehensive offering, but I’m fine with a more simple tool like PA if it means I can get my work done effectively.

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