Indigo team (that eye)¶
Machine Learning projects tend to follow a familiar arc: big promises, bigger slide decks, and then—somewhere between prototype and production—an awkward silence. Usually around the time someone asks, “Wait, is this actually solving anything?”
This roadmap is for those who’d rather wrestle with real-world problems than endlessly “solution” their way into technical debt. It’s a pragmatic guide to building MLOps systems that don’t just automate nonsense at scale, but actually help people think, act, and question wisely. From data wrangling to deployment, it’s structured to support critical engagement—not just cranking out models like sausages in a regulatory guessing game.
Skip the bollocks, solve real problems.
Use it to steer clear of hand-wavy AI hype, reduce the cycle of reinventing the same wheel, and maybe—just maybe—build something useful on purpose that isn’t complicit in turning the (digital) world into a panopticon or worse.