Learning Automl : Automating Ml Pipelines With Autogluon, Leading Frameworks, and Real-world Integration

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 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.