1. The Model That Disappeared: A Lesson in Architectural Debt The most costly failures in machine learning are rarely mathematical; they are structural. Consider the common engineering tragedy: after exhaustive data cleaning and high-compute training, a developer achieves a model with 98% accuracy. It is a technical triumph until the environment is reset or the session terminates. Without a persisted state, documented weights, or a reproducible path, the model is effectively vaporware. This frustration highlights a critical industry bottleneck: the tendency to view machine learning as a "science project" rather than a production asset. Shifting toward high-signal engineering requires acknowledging that ML is not merely a collection of algorithms—it is a rigorous, multi-stage lifecycle. Without a strategy for architectural durability and idempotency, even the most accurate models represent little more than technical debt. 2. Machine Learning is a Lifecycle, Not a One-Off Event...