MLOPs From Notebook To Pipeline

While most books teach you what MLOps is, this one teaches you how to build it step by step, with a professional folder structure you can copy and paste into your next project. Stop creating experiments. Start building solutions. Many data scientists are experts at experimentation, but they stop at the "chasm" of production deployment. This book is the bridge you need to cross it. Who is this book for? This book is written for Data Scientists, Analysts, and Developers who already know how to program in Python and understand the basics of Machine Learning, but who feel lost when they hear terms like Docker, API, CI/CD, or Kubernetes. You don't need to be an infrastructure expert; this book will teach you from scratch how a software engineer thinks. What will you find inside? Through a practical case study, we'll move beyond notebook scripts to build a pipeline. You'll learn to: • Modularize your code: Break down the notebook into logical and reusable pieces. • Create Data Contracts: Use Pydantic for data quality control. • Containerize with Docker: Package your solution for deployment. • Deploy real APIs: Create services with Streamlit or FastAPI. • Orchestrate and Monitor: Understand when and how to use Airflow and mlflow. Why is this book different? Unlike other dense, theoretical manuals, this is an executive and radically practical book. We won't waste time on endless definitions. Here, every theoretical concept is immediately translated into a .py file, a terminal command, or a working container.