The ML Lifecycle
Pipelines, Not Notebooks
The notebook is a lab, not a factory
Notebooks are where models are born — and where production systems go to die. Out-of-order cell execution, hidden kernel state, data_final_v3_CLEAN.csv loaded from someone's laptop. Perfect for exploration; indefensible as a system.
A production pipeline is a directed acyclic graph of stages: ingest → validate → transform → train → evaluate → gate → deploy. Each stage declares its inputs, produces versioned artifacts, and runs unattended on a schedule or a trigger. Nothing depends on which cells a human happened to execute, in which order, three months ago.
analogy
A notebook is a stained recipe card in your own handwriting. A pipeline is the commercial kitchen that can reproduce the dish at 3am without you — and the kitchen is what you're actually shipping.