Learning AutoML - Automating ML Pipelines with AutoGluon, Leading Frameworks, and Real-World Integration (Kerem Tomak)（Z-Library）

Kerem Tomak Foreword by Thomas H. Davenport Learning AutoML Automating ML Pipelines with AutoGluon, Leading Frameworks, and Real-World Integration

9 7 9 8 3 4 1 6 4 3 1 8 5 5 7 9 9 9 US $79.99 CAN $99.99 DATA ISBN: 979-8-341-64318-5 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 Dr. Kerem Tomak is the founder and CEO of MindspaceAI, a boutique ML/AI consulting and AI product development company based in Amsterdam. Previously, he held executive positions at Decathlon, ING, Commerzbank AG, and Google. With a PhD from Purdue and multiple machine learning patents, he brings deep expertise in scaling AI systems across finance, retail, and tech. Learning AutoML “I’ve watched a lot of teams waste enormous energy on ML plumbing that AutoML should have handled. What Kerem Tomak gets right— and most resources miss—is that this isn’t about laziness or shortcuts. It’s about where human judgment actually adds value. In today’s world, understanding AutoML at this depth isn’t optional—it’s foundational.” Ashkan Roshanayi, CEO, DataChef

Praise for Learning AutoML I’ve watched a lot of teams waste enormous energy on ML plumbing that AutoML should have handled. What Kerem Tomak gets right—and most resources miss—is that this isn’t about laziness or shortcuts. It’s about where human judgment actually adds value. In a world where agentic systems compose, retrain, and evaluate models on the fly, understanding AutoML at this depth isn’t optional—it’s foundational. The case studies show what it looks like when that boundary is drawn correctly: faster iteration, better models, and teams that can finally think about the problem instead of the pipeline. —Ashkan Roshanayi, CEO, DataChef What sets Kerem’s work apart is that he never treats AutoML as technology for technology’s sake. Throughout the book—from the ROI analyses and industry-specific business cases in the opening chapters to the case studies where he maps every percentage point of model improvement to concrete financial outcomes like freed capital, recovered revenue, and operational savings—Kerem consistently answers the question that matters most to business leaders: “So what does this mean for my bottom line?” This is a rare and valuable quality in a technical book, and it makes Learning AutoML essential reading not just for data scientists, but for anyone responsible for turning AI investments into actual business results. —Baris Kavakli, CEO, Portera

Kerem Tomak Learning AutoML Automating ML Pipelines with AutoGluon, Leading Frameworks, and Real-World Integration

979-8-341-64318-5 [LSI] Learning AutoML by Kerem Tomak Copyright © 2026 Kerem Tomak. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Aaron Black Development Editor: Shira Evans Production Editor: Christopher Faucher Copyeditor: Arthur Johnson Proofreader: Andrea Schein Indexer: Krsta Technology Solutions Cover Designer: Susan Brown Cover Illustrator: José Marzan Jr. Interior Designer: David Futato Interior Illustrator: Kate Dullea April 2026: First Edition Revision History for the First Edition 2026-04-03: First Release See http://oreilly.com/catalog/errata.csp?isbn=9798341643185 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Learning AutoML, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

Table of Contents Foreword by Thomas H. Davenport. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Foreword by Gregory Wheeler. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Part I. Foundations of AutoML 1. What Is Automated Machine Learning?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The Growing Demand for Machine Learning Solutions 4 Addressing the Data Science Talent Gap 6 Democratizing AI Development 8 AutoML in the Machine Learning Landscape 10 Who Should Use AutoML? 14 AutoML Across Industries: Transforming Business Processes 15 Overcoming Hurdles: Persistent Challenges in AutoML 19 The Horizon: Future Trends Shaping AutoML 21 Summary 23 2. The Rise and Current State of AutoML. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Early Automation (Pre-2010): Laying the Groundwork 26 First Generation (2010–2015): Solving the CASH Problem 27 Second Generation (2015–2020): Solving the Usability and Enterprise Problem 31 Third Generation (2020–Present): Solving the Multimodal and MLOps Problem 37 Summary 43 v

3. Understanding the AutoML Pipeline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 The Architecture of Automated Machine Learning 48 Data Preprocessing 51 Feature Engineering 53 Hyperparameter Optimization 55 Neural Architecture Search 58 Model Selection, Ensembling, and Stacking 61 Model Deployment and Monitoring 64 Pipeline Integration and Optimization 67 Challenges and Future Directions 69 Summary 70 Part II. Core AutoML Techniques 4. Automated Data Preprocessing and Feature Engineering. . . . . . . . . . . . . . . . . . . . . . . 75 Working Dataset: RetailMart Ecommerce Platform 76 Intelligent Data Profiling and Quality Assessment 78 Smart Data Type Handling and Transformation 85 Automated Feature Engineering 89 Intelligent Feature Selection and Dimensionality Management 94 Preprocessing Complex and Multimodal Data 96 Production-Ready Preprocessing Pipelines 100 Summary 104 5. Hyperparameter Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 The Challenge of Hyperparameter Optimization 108 Grid Search Versus Random Search: Building the Foundation 111 Bayesian Optimization: Learning from Experience 115 Early Stopping and Scheduling: Working Smarter, Not Harder 123 Multifidelity Optimization: Beyond Simple Early Stopping 131 Case Study: Personal Investment Portfolio Optimization with Multifidelity HPO 134 Key Insights and Practical Considerations 139 Summary 141 6. Neural Architecture Search (NAS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Understanding Neural Architecture Search 146 The Three Pillars of NAS 147 Search Space Design: Defining the Boundaries 148 Search Strategies: Finding Needles in Haystacks 153 Performance Estimation: The Efficiency Imperative 159 vi | Table of Contents

Efficient NAS: Making It Practical 166 Practical Applications and Tools 176 Summary 184 Part III. AutoML for Different Data Types 7. AutoGluon for Tabular Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Setting Up AutoGluon and Environment 190 Choosing the Right AutoML Framework for Tabular Data 195 TabularPredictor Basics 196 Binary and Multiclass Classification 201 Regression Tasks 204 AutoGluon’s Automatic Data Processing 206 Advanced Customization 207 Model Interpretability and Debugging 210 Project: Titanic Survival Prediction 212 Data Pipeline Consistency 220 Monitoring and Maintaining Models in Production 221 Summary 222 8. AutoML for Text and Natural Language Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 AutoGluon’s MultiModalPredictor for Text Processing 226 Building Text Classification Models 229 Advanced Text Processing Capabilities 232 The Transformer Revolution and Beyond 236 Domain-Specific Considerations 238 Model Selection for Different Use Cases 239 Real-World Applications and Performance 241 Production Deployment Considerations 243 Monitoring and Maintenance 247 Practical Project: News Article Classification 253 Summary 257 9. Time Series Forecasting with AutoGluon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Understanding the Time Series Challenge 260 Getting Started with TimeSeriesPredictor 262 Foundation Models and Zero-Shot Forecasting 265 Handling Complex Multiseries Scenarios 267 Advanced Capabilities: Covariate Regressors 269 Model Selection and Hyperparameter Optimization 271 Evaluation and Validation Strategies 274 Table of Contents | vii

Production Deployment and Cloud Integration 276 Practical Project: Retail Demand Forecasting 290 Future Directions and Emerging Capabilities 293 Summary 294 10. Computer Vision with AutoGluon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Understanding AutoGluon’s Computer Vision Capabilities 298 The MultiModalPredictor Advantage 302 Foundation Models Integration 303 Setting Up AutoGluon for Computer Vision 305 Image Classification with MultiModalPredictor 308 Object Detection with AutoGluon 314 Multimodal Computer Vision Applications 317 Real-World Computer Vision Project: Automated Ecommerce Product Classification 320 Performance Optimization and Best Practices 327 Production Deployment Considerations 332 Summary 345 Part IV. Production and MLOps 11. Workflow Integration with MLOps Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Understanding the AutoML-MLOps Integration Landscape 352 Experiment Tracking and Model Management 354 Workflow Orchestration with Kubeflow 358 Production Deployment Patterns 362 Monitoring and Governance 367 Integration Challenges and Solutions 371 Best Practices and Implementation Guidelines 374 Summary 376 12. Data Pipeline Automation with Apache Airflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Understanding Data Pipeline Requirements for AutoML 380 Airflow Architecture for Machine Learning Workflows 383 Designing DAGs for AutoML Data Ingestion 387 Practical Example: Complete AutoML Data Ingestion DAG 388 Dynamic Task Mapping for Parallel Processing 393 Feature Engineering Pipelines and Feature Stores 394 Handling Late-Arriving Data 396 Data Contracts and Schema Evolution 397 Monitoring and Data Quality Gates 399 viii | Table of Contents

Scaling Airflow for Enterprise AutoML 402 Operational Excellence and Best Practices 404 Summary 406 13. Deployment and Continuous Delivery for AutoML. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 The Unique Challenges of AutoML Deployment 408 Continuous Integration for Machine Learning 409 Continuous Deployment Pipelines 414 Testing Strategies for Automated Models 417 Model Packaging and Containerization 419 Practical Example: Deploying the Adult Income Prediction Model 421 Model Serving Infrastructure 430 Monitoring and Observability in Production 434 Security and Compliance Considerations 440 Continuous Learning and Feedback Loops 442 Summary 450 Part V. Case Studies 14. Case Study 1: Financial Services—Real-Time Fraud Detection at GlobalBank. . . . . . 461 Business Problem and Context 461 Data Pipeline and Preparation 462 Feature Engineering 466 Model Development with AutoGluon 471 Model Evaluation and Interpretability 474 Deployment Architecture 477 Monitoring and Maintenance 481 Outcomes and Lessons Learned 482 Summary 483 15. Case Study 2: Retail—Omnichannel Demand Forecasting. . . . . . . . . . . . . . . . . . . . . . 485 Business Problem and Context 485 Data Challenges: Multisource Integration 487 Feature Engineering: Capturing Demand Drivers 491 Model Development: AutoGluon for Time Series at Scale 495 Evaluation: Business Metrics over Model Metrics 498 Deployment: Production Forecasting Pipeline 500 Business Outcomes and Lessons Learned 505 Summary 508 Table of Contents | ix

16. Case Study 3: Healthcare—Patient Readmission Prediction. . . . . . . . . . . . . . . . . . . . 509 The Business Challenge 509 Data Challenges and HIPAA Compliance 511 Feature Engineering: Structured and Unstructured Data 516 Model Development: Fairness-Aware AutoML 521 Evaluation: Performance + Fairness Metrics 525 Deployment: Clinical Workflow Integration 532 Monitoring: Drift and Fairness 536 Business Outcomes and Lessons Learned 538 The Production AutoML Blueprint: A Grand Synthesis 542 Summary 544 Epilogue: The Quiet AutoML Revolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 x | Table of Contents

Foreword by Thomas H. Davenport Automated machine learning is a difficult subject to write about. It’s a relatively easy concept to grasp at the highest level—“Wouldn’t it be great if a computer could auto‐ matically create a statistical model to fit my data well and make great predictions?— but difficult to address in detail from both organizational and technical perspectives. In fact, most experts on AutoML are quite technical in their backgrounds and orien‐ tations, and aren’t really able to discuss the organizational and economic implications at all. This book is different in that it ably discusses both perspectives on the topic. Kerem Tomak is a senior business executive with a hardcore data science background, and he’s able to bridge the two different domains of AutoML. Nevertheless, I would take his advice about what sections of this book to read given your particular background and approach to this topic. Despite the two worlds of the topic that need to be connected, this is an exciting time to write and read a book on AutoML. Professional data scientists, once wary of AutoML because they thought they could create better models “by hand,” have begun to embrace the technology—particularly for early-stage model exploration. Among nonprofessionals there has also been an exciting set of new technology devel‐ opments. Some AutoML programs were already pretty easy to use by amateurs, with point-and-click interfaces and integration with business intelligence programs. But now generative AI has enabled not only “vibe coding,” but also “vibe data science.” As Tomak notes in the book, the use of language model prompts to create machine learning models is just the latest step in a long series of technological developments that have enabled greater democratization of data science and machine learning. It’s now possible for non-technical users to issue a short prompt to a language model, upload a dataset and quickly receive not only a predictive model that fits the data, but also some feature engineering, consideration of how best to treat missing values, mul‐ tiple algorithm explorations, and even a couple of pages of how a manager should address the results of the model for maximum benefit. xi

This book doesn’t devote a lot of attention to generative AI-enabled machine learn‐ ing, because it’s early days for that technology and minor variations in prompts can yield quite different results. However, Tomak does discuss at various points the important topic of when it is appropriate for amateurs to do data science and when professionals are necessary. And while the technologies he focuses on for AutoML projects are somewhat more difficult to learn than a genAI prompt, they are both more accurate and much easier to use than machine learning programs used to be. You should also be aware that this book is about the more traditional type of machine learning-based AI. Now technically speaking, generative AI is a form of machine learning. But this book is about what I call “analytical AI”—the development of machine learning models that use structured numerical data to produce predictions of other numerical data. This type of AI has been around for much longer than gen‐ erative AI. If you’ve picked up this book you probably know it’s still quite popular and important, though less well-known to the general public. In fact, I’ve done some recent research suggesting that most of today’s organizations tend to get more eco‐ nomic value from analytical AI than from generative AI. And the value of analytical AI only multiplies when an organization uses AutoML to create and manage machine learning models. Besides, there is no shortage of books on generative AI. So I hope you will read this book and then accelerate and democratize your own and your organization’s use of machine learning with AutoML. You can also manage the resulting models effectively over time and ensure that they are still good predictors of the outcomes you want to predict. There is no better guide to the topic than Kerem Tomak, and he has pulled together all the knowledge you need to become an AutoML expert. — Thomas H. Davenport Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy Author or coauthor of All In on AI, Working with AI, Agentic AI, and Competing on Analytics xii | Foreword by Thomas H. Davenport

Foreword by Gregory Wheeler Automated machine learning has been with us, in one form or another, for over a decade. The algorithms are mature. The frameworks are powerful. The cloud plat‐ forms compete fiercely for your business. And yet most organizations still struggle to move from a promising model on a laptop to a system that a clinician, a regulator, or a financial analyst can trust. Filling that gap between validation score and earned trust is the central aim of this book. It is well overdue. The gap has persisted because production machine learning is not a harder version of the modeling problem; it is a different problem altogether. A model on a laptop answers a statistical question. A model in production answers to stakeholders, regula‐ tors, and the patients or customers whose lives it touches. It must be monitored for drift, audited for fairness, explained to people who have never heard of gradient boosting, nor care to, and then be maintained by teams who did not build it. None of this is algorithmic. Most of it is not, strictly speaking, technical. And yet until now, the AutoML literature has been written almost entirely as if it were. Kerem Tomak brings a rare combination of experience to this subject. He has built production systems, developed products using AutoML in his own company, collabo‐ rated with leading lights in analytics and AI, and taught these concepts to diverse audiences, from data science graduate students to tech leads to C-suites. He writes as someone who has sat with the hard problems long enough to know which matter, which to let go, and more importantly, which ones textbooks tend to overlook. The result is a book that follows a model from raw data through hyperparameter optimi‐ zation and neural architecture search, but then keeps going, through Airflow DAGs and Kubeflow pipelines, through CI/CD for machine learning, through monitoring and drift detection in production. Kerem understands that getting a model into pro‐ duction is not the end of the story. It is just the beginning. The three case studies that close the book bring everything together. A bank process‐ ing fifty million transactions a day under hundred-millisecond latency constraints. A retailer forecasting demand across hundreds of stores and tens of thousands of SKUs. xiii

A hospital system predicting patient readmission while navigating HIPAA compli‐ ance and algorithmic fairness across demographic groups. These are not toy exam‐ ples. They are the kind of problems that reveal whether you have understood AutoML deeply enough to deploy it responsibly. The healthcare case study deserves particular attention. Tomak walks the reader through three successive approaches to bias mitigation—removing protected attributes, adversarial debiasing, and finally a fairness-aware ensemble with post-hoc calibration—showing concretely why the naïve solutions fail. The demonstration that removing race from the feature set does not prevent proxy discrimination through ZIP codes and insurance type is presented with a clarity and practicality that will stay with you. In regulated industries, the distance between knowing that proxy discrimi‐ nation exists and knowing how to detect and mitigate it in a production pipeline is the difference between a conference paper and a deployed system. Whether you are a data scientist looking to deepen your implementation skills, a domain expert building your first pipeline, or an engineer tasked with getting AutoML into production, you will find here a guide that respects both the difficulty of the problem and the intelligence of the reader. I am glad Kerem wrote it, and I am glad you are about to read it. — Gregory Wheeler Professor of Computational Science & Philosophy Frankfurt School of Finance & Management February 2026 xiv | Foreword by Gregory Wheeler

Preface Why I Wrote This Book Throughout my career spanning decades in data science and analytics, I’ve witnessed a remarkable transformation in how organizations approach machine learning. What once required teams of PhD-level experts and months of painstaking manual work can now be accomplished by domain experts in days or even hours. This democra‐ tization of machine learning capabilities through AutoML represents one of the most significant shifts I’ve observed in the field. Yet despite AutoML’s growing importance, I noticed a critical gap in available resources. Most AutoML documentation focuses on specific tools or provides high- level overviews without practical implementation guidance. Academic papers dive deep into algorithmic details but often lack real-world context. What was missing was a comprehensive resource that bridges theory and practice—one that explains not just how AutoML works but also when and why to use different approaches and how to integrate them into actual business workflows. This gap became particularly apparent in my work within organizations across finance, healthcare, retail, and tech. Time and again, I encountered talented profes‐ sionals who deeply understood their domain problems but struggled to navigate the AutoML landscape effectively. They needed guidance on selecting the right tools, understanding the trade-offs between different approaches, and implementing solu‐ tions that would hold up in production—especially in regulated industries, where explainability, trust, and governance matter. I gained much of the hands-on experience behind this book while experimenting with the tools it covers and using AutoGluon to build a product for my company, med-essence.de. I found AutoGluon to be a fit for our purpose and used it to put one of our use cases in production. While I was able to gather a lot of information from the internet and various papers and books, I also had to learn from GitHub pages, xv

fragmented resources, and sometimes incomplete documentation of open source software through testing and validation. This book grew out of that multiyear effort. My recent collaboration with Thomas Davenport and Ian Barkin on a topic exploring how AI tools enable “citizen developers” further reinforced this idea. We observed that while technology is rapidly democratizing AI development, the gap between available tools and practical knowledge remains substantial. Organizations need more than just access to AutoML platforms—they need a clear understanding of how to use these tools strategically and responsibly. How these citizen-developed solutions make their way into production is also a gray area. This book represents my attempt to fill that knowledge gap. Drawing from years of hands-on experience implementing analytics solutions, developing products using traditional and AutoML tools like AutoGluon and TPOT, teaching these concepts to diverse audiences, and observing what works (and what doesn’t) in real-world deployments, I’ve crafted a resource that serves both newcomers seeking to under‐ stand AutoML fundamentals and experienced practitioners looking to deepen their implementation skills. Rather than advocating for any particular tool or approach, this book provides a bal‐ anced perspective on the AutoML ecosystem. You’ll find detailed coverage of core concepts, such as hyperparameter optimization and neural architecture search, alongside practical tutorials using AutoGluon, one of the most capable and accessible AutoML frameworks available today. Most importantly, you’ll gain the knowledge needed to make informed decisions about when and how to apply AutoML in your specific context. 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 read‐ ers 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 tech‐ nical 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. xvi | Preface

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 hap‐ pening under the hood. Software engineers and application developers who need to integrate machine learn‐ ing capabilities into their applications will benefit from the production-focused sec‐ tions 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 capabili‐ ties and limitations is crucial for making informed decisions about AI investments and team development. Consultants and solution architects working with multiple organizations will appreci‐ ate 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 prac‐ tical guidance you need to succeed. Preface | xvii

How This Book Is Organized This book is structured as a comprehensive journey, spanning from AutoML funda‐ mentals to advanced implementation and production deployment. The five main parts build upon each other systematically to take you from theory to practice. How‐ ever, experienced readers may choose to focus on specific sections based on their immediate needs. Part I: Foundations of AutoML Part I establishes the conceptual groundwork for the rest of the book: • Chapter 1 introduces the core concepts of AutoML, explores why it matters for modern organizations, and examines its role in democratizing AI development. • Chapter 2 traces the evolution of AutoML through three distinct generations, from early academic tools to today’s enterprise-ready platforms. • Chapter 3 introduces the end-to-end AutoML pipeline, providing a complete roadmap from raw data to a deployed model. This chapter serves as the organiz‐ ing framework for the technical deep dives that follow. Part II: Core AutoML Techniques Part II dives deep into the algorithms and methods that power modern AutoML sys‐ tems, following the logical flow of the pipeline introduced in Part I: • Chapter 4 examines automated feature engineering and data preprocessing, which are crucial capabilities that often determine the success of models in real- world applications. • Chapter 5 provides comprehensive coverage of hyperparameter optimization (HPO), the foundational tuning technique underlying virtually all AutoML tools. • Chapter 6 explores neural architecture search (NAS), showing how modern sys‐ tems can automatically design optimal neural network architectures for complex tasks. Together, these chapters provide the technical foundation necessary to understand how AutoML operates under the hood and to make informed decisions about cus‐ tomizing automated workflows. xviii | Preface