Learning AutoML

Kerem Tomak

ISBN 13: 9798341643185

Published by O'Reilly Media, US, 2026

About this Item

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 dataDeploy models with fast, scalable workflows using MLOps best practicesCompare and navigate today's leading AutoML platformsInterpret model results and make informed decisions with explainability toolsExplore how AutoML leads into next-gen agentic AI systems.

Seller Inventory # LU-9798341643185

Bibliographic Details

• Title: Learning AutoML
• Publisher: O'Reilly Media, US
• Publication Date: 2026
• Language: English
• ISBN 13: 9798341643185
• Binding: Paperback
• Condition: New

About this title

Synopsis

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

About the Author

Dr. Kerem Tomak is a seasoned AI and data science leader with extensive experience implementing machine learning solutions at scale. As Global Chief Data & Analytics Officer at Decathlon and former executive at companies like ING, Commerzbank AG, and Google, he has led the design and deployment of automated, AI-powered systems across finance, retail, and tech. With a Ph.D. in Management Information Systems from Purdue University, multiple patents in machine learning, and a history of driving enterprise AI adoption, Dr. Tomak brings deep expertise to the evolving field of AutoML.