Implementing the MLOps roadmap: A pilgrimage¶
The MLOps roadmap is that sacred scroll of promises, where “robust” and “scalable” are etched in gold leaf, only to be smudged by the sweaty palms of reality. Here, we chart the perilous journey from “Look, my Jupyter notebook works!” to “Why is production on fire?”—because nothing says “mature ML process” like debugging a containerized model at 3 a.m. while questioning life choices.
Our quest has three acts: ML Development (where hope blooms), Transition to Operations (where hope meets bureaucracy), and Operations (where hope goes to die quietly in a corner). Each phase is a masterclass in “This seemed easier in the tutorial.”
Buckle up.
Somewhere between the chaos and the sarcasm, ML does ship. Sometimes. If you squint. The roadmap isn’t about perfection—it’s about failing less catastrophically each time. Or, as the ancients whispered: “Good enough is the enemy of ‘Oh God, not again.’”