| This is experience and learning from each other. Sure, there are general guidelines that can be applied in many cases (testing, keep it simple, ...). However, many things depend on the company and the team you are working in. A lot about production grade code is also to make sure it's easy to read and understand (thus maintainable). A good question to ask is also "What happens when X goes wrong?". What happens to the system in general, how critical is it, and how can you understand what happened. This is about logging and moire general observability. In the AI/ML field production grade code doesn't necessarily mean that you need to have super abstract things. A lot of this is actually the MLOps part: getting the data to your model, saving the model in some artifactory, running inference, ... . To make this easier it's most important that your code is somewhat modular and not just a plain script that you run when you need it. Also, don't be to hard on yourself. Your last two lines sound a lot like imposter syndrome. Look back at your code from 4 years ago and see how it has changed and how much you have learned. |