Publisher's Synopsis
In the rapidly evolving landscape of artificial intelligence, the success of machine learning projects hinges not just on building accurate models, but on managing the entire lifecycle-from problem definition and data preparation to deployment and monitoring. This book, Machine Learning Lifecycle & MLOps Essentials, aims to provide readers with a structured, end-to-end understanding of how modern ML systems are built, deployed, and maintained in real-world settings. By combining practical Python implementations with foundational concepts, this book equips data scientists, engineers, and ML practitioners with the tools and mindset needed to bridge the gap between experimentation and production.
As the industry embraces operational efficiency, reproducibility, and scalability, MLOps (Machine Learning Operations) has emerged as a critical discipline. This book not only introduces the lifecycle phases of ML projects but also dives into the principles and best practices of MLOps-from automation and CI/CD pipelines to monitoring drift and ensuring model governance. Whether you're a beginner eager to learn how to structure ML projects effectively or a professional aiming to implement production-grade ML workflows, this book serves as a practical guide and reference throughout your machine learning journey.
