Learning AutoML: Automating ML Pipelines with AutoGluon, Leading Frameworks, and Real-World Integration - Tapa blanda
Sinopsis
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
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Learning Automl : Automating Ml Pipelines With Autogluon, Leading Frameworks, and Real-world Integration
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Paperback. Condición: new. Paperback. 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 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798341643185
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Learning AutoML
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Paperback. Condición: New. 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. Nº de ref. del artículo: LU-9798341643185
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Learning AutoML
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Paperback. Condición: New. 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. Nº de ref. del artículo: LU-9798341643185
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