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Understanding Machine Learning Lifecycle

Building ML systems isn't like building regular software and trying to force traditional development methodologies onto ML projects is a recipe for frustration. Traditional Software Development Lifecycle (SDLC) practices are often too rigid for the messy, experimental reality of machine learning work. Here's the thing: ML is fundamentally iterative. You don't just write code, test it, and ship it. You experiment, learn from data, tweak your approach, and repeat often many times.