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Machine learning has become a critical component of modern technology, shaping industries from healthcare and finance to marketing and entertainment. Yet, building effective machine learning systems is about more than just selecting the right algorithm; it requires a holistic approach that considers design, scalability, deployment, and ongoing maintenance. This book, Designing Machine Learning Systems, offers readers a comprehensive guide to creating resilient and scalable machine learning systems that can deliver real-world results. Whether you're an engineer, data scientist, or product manager, this book is designed to bridge the gap between theory and practice, emphasizing system design principles crucial for long-term success. Through a step-by-step approach, we explore key topics such as data engineering, model selection, and the deployment lifecycle. Each chapter provides insights into best practices, tools, and frameworks that simplify the process of taking machine learning from experimentation to production. With a focus on reliability, scalability, and performance, this book aims to equip readers with a practical toolkit to build robust machine learning systems capable of handling complex demands. By the end, readers will not only understand the technical foundations but also gain the confidence to design, deploy, and monitor machine learning systems that align with real-world business objectives.