Learning AutoML

Kerem Tomak

ISBN 13: 9798341643185

Published by O'reilly Media Apr 2026, 2026

About this Item

Neuware -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 systemsLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 400 pp. Englisch.

Seller Inventory # 9798341643185

Bibliographic Details

• Title: Learning AutoML
• Publisher: O'reilly Media Apr 2026
• Publication Date: 2026
• Language: English
• ISBN 13: 9798341643185
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 176H x 33D millimeters 231 millimeters width by 176 millimeters height by 33 millimeters depth
• Weight: 1014 grams

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.