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

“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

Table of Contents

Part I. Foundations of AutoML

Chapter 1. What Is Automated Machine Learning?

Chapter 2. The Rise and Current State of AutoML

Chapter 3. Understanding the AutoML Pipeline

Part II. Core AutoML Techniques

Chapter 4. Automated Data Preprocessing and Feature Engineering

Chapter 5. Hyperparameter Optimization

Chapter 6. Neural Architecture Search (NAS)

Part III. AutoML for Different Data Types

Chapter 7. AutoGluon for Tabular Data

Chapter 8. AutoML for Text and Natural Language Processing

Chapter 9. Time Series Forecasting with AutoGluon

Chapter 10. Computer Vision with AutoGluon

Part IV. Production and MLOps

Chapter 11. Workflow Integration with MLOps Tools

Chapter 12. Data Pipeline Automation with Apache Airflow

Chapter 13. Deployment and Continuous Delivery for AutoML

Part V. Case Studies

- What Went Wrong?

- The Reality of Production AutoML

- What Makes These Case Studies Different

- Case Study Structure

- How to Use These Case Studies

- Connecting to Your AutoML Journey

- A Note on the Projects

Chapter 14. Case Study 1: Financial Services—Real‑Time Fraud Detection at GlobalBank

Chapter 15. Case Study 2: Retail—Omnichannel Demand Forecasting

Chapter 16. Case Study 3: Healthcare—Patient Readmission Prediction

About the Author

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.