zanew101

Maybe this is something you want to keep internal to your company, but can you say anything about the model architecture being used here?

I work in an research setting with access to lots of skilled software engineers and I've come to a very similar conclusion. The problem I often see is that there is very little incentive for the researcher to bring a software engineer onto their project unless it is absolutely necessary. It is just now becoming commonplace to release code when you publish and plenty of researchers still don't.

If you think no one is going to see your code then it makes it much harder to care about its quality. One way we've been fighting this internally is by trying to get researchers less silo'd and more open to code reviews. Once a code review is part of the process then bringing on an "expert coder" to address some of the issues that come up in the reviews has more tangible results! We've seen some success in improving our code quality with this strategy.

Hah, classic. But in all seriousness I think its a pretty interesting issue. A lot of the ML and data science sees people coming in who do not have formal computer science and software development backgrounds. We build tools and methodologies around abstracting away some of the code development process and hope that it lands us in an environment that's easy to share with others. This is unfortunately rarely the case.

Its a problem that as an industry I think we are in the middle of "solving" (probably can't be solved fully, but things are getting better). I'm really excited to see what kinds of tools and tests will be developed around getting ML projects with some better practices.