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Learning Automl: Automating ML Pipelines with Autogluon, Leading Frameworks, and Real-World Integration
Paperback
Publisher Price: $79.99
Publisher: O'Reilly Media
Published: May 12 2026
Pages: 590
Weight: 2.05
Height: 1.20 Width: 7.00 Depth: 9.19
Language: English
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
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