MLOps: A field guide to impending disasters¶

Welcome, brave soul, to the treacherous world of MLOps—where good intentions collide with technical debt, where “best practices” are often just wishful thinking scrawled on a whiteboard, and where every deployment is a roll of the dice.

This guide is not here to sell you the dream of “seamless AI integration.” No, we’re here to expose the gremlins lurking in your pipelines, the time bombs ticking in your model registries, and the budgetary black holes disguised as “scalable solutions.” Whether you’re a wide-eyed start-up or a corporate juggernaut, the laws of MLOps entropy apply: things will break, costs will spiral, and someone will eventually ask, “Why didn’t we foresee this?”

This is not a manifesto of despair, but a survival guide. Forewarned is forearmed—or at least fore-resigned.