Textbook in PDF format Learning AutoML is your practical guide to applying automated machine learning in real-world environments. Whether you're a data scientist, ML engineer, or AI researcher, this book helps you move beyond experimentation to build and deploy high-performing models with less manual tuning and more automation. Using AutoGluon as a primary toolkit, you'll learn how to build, evaluate, and deploy AutoML models that reduce complexity and accelerate innovation. Author Kerem Tomak shares insights on how to integrate models into end-to-end deployment workflows using popular tools like Kubeflow, MLflow, and Airflow, while exploring cross-platform approaches with Vertex AI, SageMaker Autopilot, Azure AutoML, Auto-sklearn, and H2O.ai. Real-world case studies highlight applications across finance, healthcare, and retail, while chapters on ethics, governance, and agentic AI help future-proof your knowledge. Build AutoML pipelines for tabular, text, image, and time series data Deploy models with fast, scalable workflows using MLOps best practices Compare and navigate today's leading AutoML platforms Interpret model results and make informed decisions with explainability tools Explore how AutoML leads into next-gen agentic AI systems Who Should Read This Book: This book is designed for anyone who wants to understand and effectively apply automated Machine Learning, regardless of their current technical background. The content is structured to serve multiple audiences simultaneously, with different readers likely to focus on various sections based on their needs and experience levels. Data scientists and ML engineers will find comprehensive coverage of AutoML’s technical foundations, comparative analysis of different optimization approaches, and advanced techniques for customizing automated workflows. Even if you’re already experienced with traditional machine learning, this book will help you understand how AutoML can accelerate your work and expand your capabilities. The sections on hyperparameter optimization, neural architecture search, and production integration provide depth that goes beyond typical tool documentation. Business analysts and domain experts represent the most important audience for this book. If you understand your organization’s data and business problems but lack extensive programming experience, the hands-on sections using AutoGluon will enable you to build sophisticated models with minimal code. The industry-specific examples and case studies will help you identify opportunities to apply AutoML in your domain, while the conceptual explanations ensure you understand what’s happening under the hood. Software engineers and application developers who need to integrate machine learning capabilities into their applications will benefit from the production-focused sections covering deployment, CI/CD, and MLOps integration. You don’t need to become a data science expert to effectively leverage AutoML. Still, you do need to understand how these systems work and how to integrate them reliably into larger software systems. Students and educators in data science, computer science, or related fields will find this book serves as both a comprehensive introduction to AutoML concepts and a practical guide for hands-on learning. The progression from fundamentals through advanced applications, combined with real-world projects and case studies, makes it suitable for both self-directed learning and classroom use. Business leaders and decision makers should focus on the foundational chapters and industry application sections to understand the strategic implications of AutoML. While you may not implement solutions directly, understanding AutoML’s capabilities and limitations is crucial for making informed decisions about AI investments and team development. Consultants and solution architects working with multiple organizations will appreciate the broad coverage of different AutoML approaches, the comparative analysis of tools and techniques, and the industry-specific guidance. This book provides the knowledge base needed to recommend appropriate solutions across diverse client contexts. The book assumes basic familiarity with data analysis concepts but doesn’t require deep machine learning expertise. Mathematical concepts are explained intuitively, with technical details provided for those who want them. Code examples use Python and focus on practical implementation rather than complex programming concepts. Whether you’re looking to automate existing Machine Learning workflows, explore new applications of AI in your organization, or simply understand how AutoML is transforming the Data Science landscape, this book provides the knowledge and practical guidance you need to succeed