Chapter 1 Introduction

Imagine being able to effortlessly deploy, manage, and monitor your machine learning models with ease. No more headaches from version control issues, data drift, and model performance degradation. That’s the power of MLOps. “MLOps Engineering: Building, Deploying, and Managing Machine Learning Workflows with Airflow and MLflow on Kubernetes” takes you on a journey through the principles, practices, and platforms of MLOps. You’ll learn how to create an end-to-end pipeline for machine learning projects, using cutting-edge tools and techniques like Kubernetes, Terraform, and GitOps, and working with tools to ease your machine learning workflow such as Apache Airflow and MLflow Tracking. Before we begin, let’s have a more closer look on what MLOps actually is, what principles it incorporates, and how it distinguished from traditional DevOps.