دانلود کتاب مقیاس‌بندی یادگیری گراف برای سازمان -یادگیری گراف و استنتاج آماده برای تولید

The primary audience for this book includes data scientists and machine learning engineers who want to go beyond training a one-off graph model and successfully productize their data science projects. You should be comfortable with basic machine learning concepts and familiar with at least one machine learning framework (e.g., PyTorch, TensorFlow, Keras). A secondary audience for this book includes managers of data science projects, software developers, and DevOps engineers who want to enable their organization to accelerate their data science projects with graph technologies. If you are interested in better understanding automated graph learning life cycles and how they can benefit your organization, this book will introduce a toolchain to achieve exactly that. In each chapter, we will introduce specific steps for building effective graph learning systems and demonstrate how these work with practical examples. Chapter 1, “Introduction to Graphs”, provides an overview of graph structures, their applications, and why they are powerful for enterprise problems. Chapter 2, “The Graph Machine Learning Pipeline”, outlines the end-to-end process for building, deploying, and maintaining graph-based machine learning systems. Chapter 3, “Traditional Machine Learning for Graphs”, explores how classical machine learning techniques can be adapted and applied to graph data. Chapter 4, “PyGraf: End-to-End Graph Learning and Serving”, introduces PyGraf, a practical framework for building and serving graph learning models in a production environment. Chapter 5, “Graph Neural Networks”, dives into the foundational concepts and architectures of graph neural networks (GNNs). Chapter 6, “Advanced Techniques in Graph Learning”, covers more sophisticated graph learning methods and their applications. Chapter 7, “Scalable Graph Neural Networks”, addresses the challenges of scaling GNNs to large, enterprise-sized datasets and discusses solutions. Chapter 8, “Enterprise Applications of Graphs”, showcases real-world use cases of graph learning across various industries, drawing from our experience. Chapter 9, “Privacy-Preserving Graph Learning”, explores techniques for building graph models while adhering to strict data privacy regulations. Chapter 10, “Graph Inference and Deployment Strategies”, focuses on deploying graph learning models efficiently for real-time and batch inference. Chapter 11, “Monitoring and Feedback Loops”, discusses how to monitor the performance of deployed graph models and establish feedback mechanisms for continuous improvement. Chapter 12, “Future Trends: Graph Learning and LLMs”, provides an outlook on emerging technologies, particularly the intersection of graph learning and large language models (LLMs), and their potential impact. As you turn the pages, try to look at each new dataset through a simple lens: how do the records affect one another, and what can those links tell us that single rows cannot? That habit is often the spark that changes a minor improvement into a breakthrough.