"The first pipeline is never perfect. But a working pipeline you can improve beats a perfect design you'll never finish. Start simple, automate relentlessly, and iterate based on what breaks." Table of Contents The Weekend That Changed Everything The Six-Step Pipeline Architecture Step 1: Data Ingestion & Validation Step 2: Data Preprocessing & Feature Engineering Step 3: Model Training & Experiment Tracking Step 4: Model Evaluation & Validation Step 5: Model Deployment & Ser

"The difference between a machine learning experiment and a production ML system is like the difference between a recipe scribbled on a napkin and a commercial kitchen that serves 10,000 meals a day. One is creative chaos; the other is systematic excellence." Table of Contents The $50 Million Jupyter Notebook What Is MLOps, Really? The Model Development Lifecycle: Six Phases That Matter Why Traditional DevOps Isn't Enough The Three Pillars: CI/CD/CT Governance: Not a Checkbox