Di Zhu – författare

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.

Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You''ll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You''ll learn the basics and advanced aspects to understand the production ML lifecycle.

This book provides four in-depth sections that cover all aspects of machine learning engineering:

Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.

Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You''ll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You''ll learn the basics and advanced aspects to understand the production ML lifecycle.

This book provides four in-depth sections that cover all aspects of machine learning engineering:

Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting-especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.This book provides four in-depth sections that cover all aspects of machine learning engineering:Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storageModeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture searchDeployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and loggingProductionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

This book, set against the background of accounts of globalisation, aims to figure out the consumer orientation of the middle class in contemporary China, in particular how the new elements in consumer orientation operate in the Chinese context. It focuses on the contemporary middle class. Data used in the book are taken from national representative surveys conducted in the recent decade and also from 30 interviews with middle class people in Beijing. The book focuses on the consumption patterns from everyday consumption, taste and material culture. It highlights consumers' self-referential orientations: the pursuit of pleasure, tempered by considerations regarding comfort, is a significant form of aesthetic justification. Living within one's means i.e. keeping a balance between expenditure and income is the main moral justification. Consumers' orientations draw on a new set of elements, conceptualised in this research as "the orientation toward personal pleasure and comfort". This orientation is shaped by social conventions, traditional values and the metropolitan context. The findings challenge the stereotype of the Chinese "new rich" and the one-dimensional pictures of tendencies towards either conspicuous display or frugality.

This book explores how Internet use empowers users and the range of economic and social implications of these impacts. This is the first systematic study of how the Internet and online social networks reconfigure the life choices and opportunity sets Chinese people of different age groups face, and ways in which internet users’ life online and offline are intertwined with each other. The new ways of obtaining and making use of information and of interacting with others made possible by new forms of technology and their applications have profound and lasting effects on not only individuals but also social groups, and the entire population. The book also discusses the complex and dynamic relationship between tools and the people who use them.