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What are decorators in Python, and what do they actually get used for in production ML code?
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See plansA decorator sits above a function and wraps it, adding behavior — timing it, retrying it, caching its result — without modifying the function’s own code. The value is separation of concerns: the retry logic doesn’t belong tangled inside your training loop, and a decorator lets you add it as a layer instead.
The one that matters most in production GenAI code is a retry decorator
with exponential backoff wrapped around LLM and embedding API calls —
those calls fail intermittently for reasons that have nothing to do
with your code, and a pipeline with no retry logic crashes on the first
transient timeout. A timing decorator around training or inference
steps is the next most common, useful for spotting regressions without
littering time.time() calls everywhere. functools.lru_cache applied
to an expensive, repeatable computation — embedding the same piece of
text twice, say — avoids redoing work for free. All three follow the
same shape: *args, **kwargs pass straight through, so the decorator
works on any function regardless of its signature.