After a long pandemic break, PyCon DE & PyData is back again! I attended previous conferences remotely, and this year was the first time when I arrived to Berlin to be there in person. The conference was great, with plenty of amazing talks and insights from leading Python engineers and data scientists. And while the memories are still fresh, I would like to structure the collected notes and briefly summarize attended presentations.
Building machine learning models isn’t easy. Heavy datasets and tricky data formats. A ton of hyper-parameters, models, optimization algorithms. Not talking about generic programming adventures like debugging, handling exceptions, and logging. It is especially true for the R&D (Research and Development) style of work when models, approaches (and sometimes, even the data itself!) can change very quickly. You wouldn’t like to invest a large amount of time and effort to build something that could become irrele
Exceptions mechanism is widely adopted among modern programming languages, and Python is not an exception. (Pun intended!) Though this topic could seem obvious, like wrap your code blocks with try-catch clauses, and that’s all, there are some minor but important details, and taking them into account should make your code a bit cleaner. In this post, I’m going through some guidelines on how to structure error processing in Python that I derived from my personal experience.