{"product_id":"139930","title":"A Practical Guide for Data and AI System Architects ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXOZV81352DMqeI6kjIj4TrzqLvhP.png?v=1765077218\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e A Practical Guide for Data and AI System Architects \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/151347071\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eAll the knowledge and practical know-how you need to become a data science expert, all in one book!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Many organizations want to incorporate data and AI technologies into their services, but implementing them in actual operational environments and ensuring stable operation is by no means simple.\u003cbr\u003e It requires the ability to organically handle the entire process, from data collection, storage, and processing to model deployment and management, and to systematically integrate and consistently operate various technologies.\u003cbr\u003e\u003cbr\u003e Practitioners often face the challenge of wanting to leverage data and AI, but not knowing where to start or how to design and configure it. \u003cbr\u003eEven if you are proficient in individual technologies, you often have difficulty connecting and operating them from a system-wide perspective.\u003cbr\u003e This often leads to repeated trial and error at various stages, from the initial design of the project to operation.\u003cbr\u003e\u003cbr\u003e This book covers a wide range of topics, from the fundamentals of data science to data engineering, model operation, system architecture design, and operational strategy.\u003cbr\u003e It presents architecture-centric, practical solutions that practitioners in various roles can refer to, covering topics frequently encountered in practice, such as log design, microservice transition, performance optimization, security, and cost management.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e [PART 01] Data Science Fundamentals\u003cbr\u003e\u003cbr\u003e ▣ Chapter 1: Understanding Data\u003cbr\u003e 1.1 Definition and types of data\u003cbr\u003e ___1.1.1 Data Types\u003cbr\u003e ___1.1.2 Data Attributes\u003cbr\u003e 1.2 Data Analysis \u003cbr\u003e___1.2.1 Statistical Modeling\u003cbr\u003e ___1.2.2 Data Analysis Process\u003cbr\u003e ___1.2.3 Data Analysis Example\u003cbr\u003e 1.3 Data Visualization\u003cbr\u003e ___1.3.1 Types of visualization\u003cbr\u003e ___1.3.2 Visualization Principles\u003cbr\u003e 1.4 Exploratory Data Analysis\u003cbr\u003e ___1.4.1 Exploratory Data Analysis Checklist\u003cbr\u003e ___1.4.2 Example of Exploratory Data Analysis\u003cbr\u003e\u003cbr\u003e ▣ Chapter 2: Basics of Machine Learning\u003cbr\u003e 2.1 Machine Learning Concepts\u003cbr\u003e ___2.1.1 Business Goals and Implementation Considerations for Machine Learning\u003cbr\u003e ___2.1.2 Defining and considering problems that can be solved with machine learning\u003cbr\u003e 2.1 Machine Learning Principles\u003cbr\u003e ___2.1.1 Forward propagation\u003cbr\u003e ___2.2.2 Activation function\u003cbr\u003e ___2.2.3 Loss function\u003cbr\u003e ___2.2.4 Optimization Algorithm\u003cbr\u003e ___2.2.5 Backpropagation\u003cbr\u003e 2.3 Model Performance Improvement and Evaluation\u003cbr\u003e ___2.3.1 Overfitting and Underfitting\u003cbr\u003e ___2.3.2 Normalization Techniques\u003cbr\u003e ___2.3.4 Model Evaluation Metrics\u003cbr\u003e ___2.3.5 Model Selection and Hyperparameter Tuning\u003cbr\u003e 2.4 Examples of Machine Learning Model Applications\u003cbr\u003e ___2.4.1 Major approaches to machine learning\u003cbr\u003e ___2.4.2 Machine Learning Application Cases\u003cbr\u003e ___2.4.3 Considerations when applying the model\u003cbr\u003e\u003cbr\u003e ▣ Chapter 3: The Core of Deep Learning \u003cbr\u003e3.1 Basic Neural Network Model\u003cbr\u003e ___3.1.1 Multilayer Perceptron\u003cbr\u003e ___3.1.2 Convolutional Neural Networks\u003cbr\u003e ___3.1.3 Recurrent Neural Networks\u003cbr\u003e 3.2 Generative and Representation Learning Models\u003cbr\u003e ___3.2.1 Autoencoder\u003cbr\u003e ___3.2.2 Generative Adversarial Networks\u003cbr\u003e 3.3 Domain-Specific Neural Network Models\u003cbr\u003e ___3.3.1 Graph Neural Networks\u003cbr\u003e ___3.3.2 Deep Q-network\u003cbr\u003e 3.4 Latest deep learning models\u003cbr\u003e ___3.4.1 Transformer\u003cbr\u003e ___3.4.2 Diffusion Model\u003cbr\u003e ___3.4.3 Large-Scale Language Models\u003cbr\u003e ___3.4.4 MoE Model\u003cbr\u003e\u003cbr\u003e ▣ Chapter 4: Deep Learning Applications\u003cbr\u003e 4.1 Natural Language Processing\u003cbr\u003e ___4.1.1 Data Preprocessing\u003cbr\u003e ___4.1.2 Model Architecture\u003cbr\u003e ___4.1.3 Model Training and Evaluation\u003cbr\u003e ___4.1.4 Core Model\u003cbr\u003e ___4.1.5 Required Papers\u003cbr\u003e ___4.1.6 Key Libraries and Tools\u003cbr\u003e 4.2 Audio Processing\u003cbr\u003e ___4.2.1 Data Preprocessing\u003cbr\u003e ___4.2.2 Model Architecture\u003cbr\u003e ___4.2.3 Model Training and Evaluation\u003cbr\u003e ___4.2.4 Core Model\u003cbr\u003e ___4.2.5 Required Papers\u003cbr\u003e ___4.2.6 Key Libraries and Tools\u003cbr\u003e 4.3 Computer Vision\u003cbr\u003e ___4.3.1 Data Preprocessing\u003cbr\u003e ___4.3.2 Model Architecture\u003cbr\u003e ___4.3.3 Model Training and Evaluation\u003cbr\u003e ___4.3.4 Core Model\u003cbr\u003e ___4.3.5 Required Papers \u003cbr\u003e___4.3.6 Key Libraries and Tools\u003cbr\u003e 4.4 Reinforcement Learning\u003cbr\u003e ___4.4.1 Data Preprocessing\u003cbr\u003e ___4.4.2 Model Architecture\u003cbr\u003e ___4.4.3 Model Training and Evaluation\u003cbr\u003e ___4.4.4 Core Model\u003cbr\u003e ___4.4.5 Required Papers\u003cbr\u003e ___4.4.6 Key Libraries and Tools\u003cbr\u003e 4.5 Recommendation System\u003cbr\u003e ___4.5.1 Data Preprocessing\u003cbr\u003e ___4.5.2 Model Architecture\u003cbr\u003e ___4.5.3 Model Training and Evaluation\u003cbr\u003e ___4.5.4 Core Model\u003cbr\u003e ___4.5.5 Required Papers\u003cbr\u003e ___4.5.6 Key Libraries and Tools\u003cbr\u003e 4.6 Data Science Roadmap\u003cbr\u003e ___4.6.1 Natural Language Processing\u003cbr\u003e ___4.6.2 Audio Processing\u003cbr\u003e ___4.6.3 Computer Vision\u003cbr\u003e ___4.6.4 Reinforcement Learning\u003cbr\u003e ___4.6.5 Recommendation System\u003cbr\u003e ___4.6.6 Extended Technology Stack\u003cbr\u003e\u003cbr\u003e [PART 02] Practical Data Science\u003cbr\u003e\u003cbr\u003e ▣ Chapter 5: Data Engineering\u003cbr\u003e 5.1 Data Collection\u003cbr\u003e ___5.1.1 Data Collection Method\u003cbr\u003e ___5.1.2 Data Collection Pipeline\u003cbr\u003e ___5.1.3 Considerations in Pipeline Design\u003cbr\u003e 5.2 Data Preprocessing\u003cbr\u003e ___5.2.3 Data Cleaning\u003cbr\u003e ___5.2.2 Data Conversion\u003cbr\u003e ___5.2.3 Feature Engineering\u003cbr\u003e ___5.2.4 Handling Data Imbalance\u003cbr\u003e ___5.2.5 Data Preprocessing Example \u003cbr\u003e5.3 Data Governance\u003cbr\u003e ___5.3.1 Data Governance Components\u003cbr\u003e ___5.3.2 Data Governance Tools\u003cbr\u003e ___5.3.3 Timing of Data Governance Tool Introduction\u003cbr\u003e\u003cbr\u003e ▣ Chapter 6: Data Storage and Design\u003cbr\u003e 6.1 Data Storage and Management\u003cbr\u003e ___6.1.1 Relational Database Management System\u003cbr\u003e ___6.1.2 NoSQL\u003cbr\u003e ___6.1.3 Vector Database\u003cbr\u003e ___6.1.4 Strategies for Maintaining Data Consistency and Integrity\u003cbr\u003e 6.2 Data Architecture Patterns\u003cbr\u003e ___6.2.1 Data Storage and Management Architecture\u003cbr\u003e ___6.2.2 Data Modeling Techniques\u003cbr\u003e ___6.2.3 OLAP and OLTP Systems\u003cbr\u003e ___6.2.4 Cloud-based data warehouse\u003cbr\u003e 6.3 Data Pipeline Design\u003cbr\u003e ___6.3.1 ETL and ELT\u003cbr\u003e ___6.3.2 Design principles for data collection, transformation, and storage stages\u003cbr\u003e ___6.3.3 Data Pipeline Design Considerations\u003cbr\u003e ___6.3.4 Optimizing Data Pipelines in Distributed Data Environments\u003cbr\u003e\u003cbr\u003e ▣ Chapter 7: Model Operation and Management\u003cbr\u003e 7.1 API Design Principles\u003cbr\u003e ___7.1.1 RESTful API\u003cbr\u003e ___7.1.2 RESTful API Design and Implementation\u003cbr\u003e ___7.1.3 Introduction to GraphQL \u003cbr\u003e___7.1.4 API Gateway Roles and Functions\u003cbr\u003e 7.2 Model Deployment\u003cbr\u003e ___7.2.1 Criteria for selecting a model deployment environment\u003cbr\u003e ___7.2.2 Model Deployment Methods and Scenarios\u003cbr\u003e ___7.2.3 Model Versioning and Rollback Strategies\u003cbr\u003e 7.3 Model Performance Monitoring\u003cbr\u003e ___7.3.1 Model Monitoring and Performance Analysis\u003cbr\u003e ___7.3.2 Model Drift Detection Methods\u003cbr\u003e ___7.3.3 Model Retraining Strategy\u003cbr\u003e 7.4 CI\/CD and MLOps\u003cbr\u003e ___7.4.1 CI\/CD Pipeline\u003cbr\u003e ___7.4.2 MLOps\u003cbr\u003e ___7.4.3 MLOps Platform\u003cbr\u003e ___7.4.4 MLOps Pipeline Design and Construction Strategy\u003cbr\u003e\u003cbr\u003e ▣ Chapter 8: Data Processing Pipeline\u003cbr\u003e 8.1 Workflow Design\u003cbr\u003e ___8.1.1 Defining Requirements and Setting Goals\u003cbr\u003e ___8.1.2 Workflow Step-by-Step Design\u003cbr\u003e ___8.1.3 Selecting a Technology Stack\u003cbr\u003e ___8.1.4 Workflow Monitoring and Improvement\u003cbr\u003e 8.2 Batch Processing\u003cbr\u003e ___8.2.1 Batch Processing Concepts and Features\u003cbr\u003e ___8.2.2 Introduction and Comparison of Batch Schedulers\u003cbr\u003e ___8.2.3 Design and Implement a Batch Workflow\u003cbr\u003e ___8.2.4 Batch Processing Optimization\u003cbr\u003e 8.3 Real-time processing\u003cbr\u003e ___8.3.1 Real-time processing concepts and features\u003cbr\u003e ___8.3.2 Introduction and Comparison of Streaming Platforms \u003cbr\u003e___8.3.3 Designing and Building a Real-Time Data Pipeline\u003cbr\u003e ___8.3.4 Real-time processing optimization\u003cbr\u003e 8.4 Batch Processing vs. Real-Time Processing\u003cbr\u003e ___8.4.1 Key Differences Between Batch and Real-Time Processing\u003cbr\u003e ___8.4.2 Guide to Selecting a Processing Method Based on System Requirements\u003cbr\u003e ___8.4.3 Hybrid Architecture\u003cbr\u003e\u003cbr\u003e [PART 03] System Architecture Design\u003cbr\u003e\u003cbr\u003e ▣ Chapter 9: Log Design and Operation\u003cbr\u003e 9.1 Log collection, storage, analysis, and visualization\u003cbr\u003e ___9.1.1 The Role of Logs\u003cbr\u003e ___9.1.2 Log Types\u003cbr\u003e ___9.1.3 Log Collection Methods and Tools\u003cbr\u003e ___9.1.4 Efficient Log Storage Strategies\u003cbr\u003e 9.2 Log Format and Management Strategy\u003cbr\u003e ___9.2.1 The Importance of Log Format Standardization\u003cbr\u003e ___9.2.2 Log format types and selection criteria\u003cbr\u003e ___9.2.3 Log Level Definition and Utilization\u003cbr\u003e ___9.2.4 Guidelines for Writing Log Messages\u003cbr\u003e 9.3 Elastic Stack\u003cbr\u003e ___9.3.1 What is the Elastic Stack?\u003cbr\u003e ___9.3.2 Elasticsearch\u003cbr\u003e ___9.3.3 Logstash\u003cbr\u003e ___9.3.4 Kibana\u003cbr\u003e ___9.3.5 Beats\u003cbr\u003e 9.4 A\/B Testing and Experimental Design\u003cbr\u003e ___9.4.1 What is A\/B testing?\u003cbr\u003e ___9.4.2 Principles of Experimental Design \u003cbr\u003e___9.4.3 Statistical Significance Test and Results Interpretation\u003cbr\u003e ___9.4.4 Procedures and tools for conducting A\/B testing\u003cbr\u003e\u003cbr\u003e ▣ Chapter 10: System Architecture\u003cbr\u003e 10.1 Considerations when Designing System Architecture\u003cbr\u003e ___10.1.1 Non-functional requirements analysis method\u003cbr\u003e ___10.1.2 Identifying and Managing Design Constraints\u003cbr\u003e ___10.1.3 Introduction to Architectural Patterns\u003cbr\u003e 10.2 Monolithic vs. Microservices\u003cbr\u003e ___10.2.1 Monolithic Architecture\u003cbr\u003e ___10.2.2 Microservices Architecture\u003cbr\u003e ___10.2.3 Comparative Analysis of Monolithic and Microservice Architectures\u003cbr\u003e ___10.2.4 Transition Strategy to Microservices\u003cbr\u003e 10.3 Distributed System Design Principles\u003cbr\u003e ___10.3.1 Understanding Distributed System Tradeoffs\u003cbr\u003e ___10.3.2 Considerations when Designing Distributed Systems\u003cbr\u003e ___10.3.3 Distributed System Fault Tolerance Design\u003cbr\u003e 10.4 Bottleneck Identification and Resolution Strategies\u003cbr\u003e ___10.4.1 Performance Bottleneck Identification Methodology\u003cbr\u003e ___10.4.2 Bottleneck Types\u003cbr\u003e ___10.4.3 System Performance Measurement and Analysis Tools\u003cbr\u003e ___10.4.4 Bottleneck Resolution Strategies\u003cbr\u003e\u003cbr\u003e ▣ Chapter 11: System Optimization and Scalability\u003cbr\u003e 11.1 Load Balancing \u003cbr\u003e___11.1.1 The need for and types of load balancing\u003cbr\u003e ___11.1.2 Load Balancing Algorithm\u003cbr\u003e ___11.1.3 Considerations when introducing a load balancer\u003cbr\u003e ___11.1.4 Load Balancers in Cloud Environments\u003cbr\u003e 11.2 Caching\u003cbr\u003e ___11.2.1 Basic Principles and Effects of Caching\u003cbr\u003e ___11.2.2 Caching Strategy\u003cbr\u003e ___11.2.3 How to maintain cache data consistency\u003cbr\u003e 11.3 Container Orchestration\u003cbr\u003e ___11.3.1 Docker container concepts\u003cbr\u003e ___11.3.2 Building and Managing Container Images\u003cbr\u003e ___11.3.3 Container Orchestration with Kubernetes\u003cbr\u003e ___11.3.4 Introducing Cloud-Based Container Services\u003cbr\u003e 11.4 Auto Scaling\u003cbr\u003e ___11.4.1 Horizontal Scaling vs. Vertical Scaling\u003cbr\u003e ___11.4.2 Setting Auto Scaling Policies and Rules\u003cbr\u003e ___11.4.3 Considerations for Autoscaling\u003cbr\u003e 11.5 Performance Measurement and Analysis Methods\u003cbr\u003e ___11.5.1 Selecting Performance Metrics\u003cbr\u003e ___11.5.2 Using Performance Analysis Tools\u003cbr\u003e ___11.5.3 Bottleneck Identification and Improvement Strategies\u003cbr\u003e ___11.5.4 Building a Performance Test Environment and Designing Scenarios\u003cbr\u003e\u003cbr\u003e ▣ Chapter 12: Building a Large-Scale Language Model System \u003cbr\u003e12.1 Selecting and Configuring Search Components\u003cbr\u003e ___12.1.1 Search-Based LLM Overview\u003cbr\u003e ___12.1.2 Comparison of Vector Search and Keyword Search\u003cbr\u003e ___12.1.3 Search Tool Comparison\u003cbr\u003e ___12.1.4 Search Performance Optimization and Scaling Strategies\u003cbr\u003e 12.2 Selecting and Configuring Generation Components\u003cbr\u003e ___12.2.1 Comparison of types and characteristics of generative models\u003cbr\u003e ___12.2.2 Comparison of major LLMs such as GPT, LLaMA, and Claude\u003cbr\u003e ___12.2.3 Criteria for selecting a generation model\u003cbr\u003e ___12.2.4 LLM Optimization Strategy\u003cbr\u003e 12.3 LLM System Architecture Configuration Strategy\u003cbr\u003e ___12.3.1 Comparison of Prompts, Contexts, and Fine-Tuning\u003cbr\u003e ___12.3.2 Single-Model vs. Multi-Model Combination Design\u003cbr\u003e ___12.3.3 API Design Principles for LLM-Based Applications\u003cbr\u003e ___12.3.4 Model Context Protocol (MCP)\u003cbr\u003e ___12.3.5 Efficiency Maximization Strategies for Cost Reduction\u003cbr\u003e 12.4 RAG System Architecture Configuration Strategy\u003cbr\u003e ___12.4.1 RAG System Overview\u003cbr\u003e ___12.4.2 RAG Architecture Construction Process\u003cbr\u003e ___12.4.3 RAG System Performance Evaluation and Improvement Strategies\u003cbr\u003e\u003cbr\u003e [PART 04] Service Operation Guidelines\u003cbr\u003e\u003cbr\u003e ▣ Chapter 13: Security and Protection\u003cbr\u003e 13.1 AI Law and Regulation \u003cbr\u003e___13.1.1 Overview of Key AI-Related Laws and Regulations\u003cbr\u003e ___13.1.2 Responsibility and Legal Risks of AI Systems\u003cbr\u003e ___13.1.3 Privacy Policy\u003cbr\u003e ___13.1.4 Operating Procedures for AI Compliance\u003cbr\u003e 13.2 Data Security Strategy\u003cbr\u003e ___13.2.1 Data Protection Principles and Security Model\u003cbr\u003e ___13.2.2 Data Encryption and Access Control\u003cbr\u003e ___13.2.3 Data Sharing and Transfer Methods\u003cbr\u003e ___13.2.4 Data Security Monitoring and Auditing\u003cbr\u003e 13.3 Data Security Incident Response and Recovery Process\u003cbr\u003e ___13.3.1 Data Breach Incident Types and Case Analysis\u003cbr\u003e ___13.3.2 Data Security Incident Response Framework\u003cbr\u003e ___13.3.3 Automated Breach Detection and Response\u003cbr\u003e ___13.3.4 Data Recovery and Recurrence Prevention Strategies\u003cbr\u003e 13.4 Privacy Policy\u003cbr\u003e ___13.4.1 Data anonymization and pseudonymization techniques\u003cbr\u003e ___13.4.2 Establishing a data collection and utilization policy\u003cbr\u003e ___13.4.3 Technical measures to protect personal information\u003cbr\u003e\u003cbr\u003e ▣ Chapter 14: Cost Management\u003cbr\u003e 14.1 Cloud Cost Optimization\u003cbr\u003e ___14.1.1 Cloud Service Cost Structure \u003cbr\u003e___14.1.2 Strategies for Reducing Model Training Costs\u003cbr\u003e ___14.1.3 Long-term cost savings\u003cbr\u003e ___14.1.4 Cost Tracking and Notifications\u003cbr\u003e 14.2 Setting Service Level Agreements\u003cbr\u003e ___14.2.1 What is an SLA?\u003cbr\u003e ___14.2.2 SLA Design and Operation Plan\u003cbr\u003e ___14.2.3 Penalty and Compensation Policy for SLA Violation\u003cbr\u003e 14.3 Cost Management and Optimization Strategies\u003cbr\u003e ___14.3.1 Cost Data Collection and Integration\u003cbr\u003e ___14.3.2 Cloud Cost Analysis and Forecasting\u003cbr\u003e ___14.3.3 Optimizing Resources and Improving Utilization\u003cbr\u003e\u003cbr\u003e ▣ Chapter 15: Disaster Recovery and High Availability Design\u003cbr\u003e 15.1 Failure Scenarios and Recovery Strategies\u003cbr\u003e ___15.1.1 Identifying and Responding to Data Pipeline Failures\u003cbr\u003e ___15.1.2 Model Serving Interruption Recovery Process\u003cbr\u003e ___15.1.3 Batch Processing System Recovery Strategy\u003cbr\u003e ___15.1.4 Real-time processing system recovery strategy\u003cbr\u003e ___15.1.5 Fault Isolation in Distributed Computing Environments\u003cbr\u003e 15.2 Data Backup and Restore Strategies\u003cbr\u003e ___15.2.1 Large Dataset Backup Architecture\u003cbr\u003e ___15.2.2 Model Checkpoint Management and Restoration\u003cbr\u003e ___15.2.3 Optimizing Incremental and Full Backups \u003cbr\u003e___15.2.4 Metadata and Feature Store Recovery Methods\u003cbr\u003e 15.3 High Availability Design Patterns\u003cbr\u003e ___15.3.1 Configuring a Multi-Cluster ML Infrastructure\u003cbr\u003e ___15.3.2 Ensuring Data Lake\/Warehouse Availability\u003cbr\u003e ___15.3.3 Model Serving Layer Redundancy Design\u003cbr\u003e ___15.3.4 Fault Recovery Mechanism of Real-Time Analysis System\u003cbr\u003e\u003cbr\u003e ▣ Appendix A\u003cbr\u003e A.1 RESTful API Practical Guide\u003cbr\u003e A.2 Redis Practical Guide\u003cbr\u003e A.3 RDBMS Practical Guide\u003cbr\u003e A.4 OpenSearch Practical Guide\u003cbr\u003e A.5 Elastic Stack Practical Guide\u003cbr\u003e A.6 Grafana + Loki + Promtail\/Agent Practical Guide\u003cbr\u003e A.7 Docker Practical Guide\u003cbr\u003e A.8 Kubernetes Practical Guide\u003cbr\u003e A.9 Apache Kafka Practical Guide\u003cbr\u003e A.10 Apache Flink Practical Guide\u003cbr\u003e A.11 Apache Airflow Practical Guide\u003cbr\u003e A.12 Apache Spark (PySpark) Practical Guide\u003cbr\u003e\u003cbr\u003e ▣ Appendix B\u003cbr\u003e B.1 Real-time processing architecture\u003cbr\u003e B.2 Batch Processing Architecture\u003cbr\u003e B.3 RAG Architecture\u003cbr\u003e B.4 Lambda Architecture\u003cbr\u003e B.5 Data Lakehouse Architecture\u003cbr\u003e\u003cbr\u003e ▣ Appendix C\u003cbr\u003e C.1 Characteristics of Data Science Projects\u003cbr\u003e C.2 Project Phase Management Strategy\u003cbr\u003e C.3 Applying Agile Methodologies \u003cbr\u003eC.4 Outputs and Management Documentation\u003cbr\u003e C.5_ Data Science Project Risk Management\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5529\/MidCate9\/552889747.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ Core theories of data science, including data analysis, visualization, and exploratory analysis.\u003cbr\u003e ◎ Overview of machine learning and deep learning model design and introduction to major application areas\u003cbr\u003e ◎ Data engineering practices such as data collection, preprocessing, integration, and governance\u003cbr\u003e ◎ Data storage design, architecture patterns, and data pipeline construction\u003cbr\u003e ◎ Workflow design, batch processing, and real-time processing system design and comparison\u003cbr\u003e ◎ API design, model deployment, CI\/CD, performance monitoring, and MLOps concepts\u003cbr\u003e ◎ Log architecture design, Elastic Stack, and experimental design (A\/B testing) principles\u003cbr\u003e ◎ System design strategies such as microservices, distributed systems, bottleneck analysis, and improvement\u003cbr\u003e ◎ System optimization and operation technologies such as caching, load balancing, and autoscaling \u003cbr\u003e◎ Case studies of the latest AI architecture, including LLM and RAG system configuration strategies\u003cbr\u003e ◎ Data Security, Privacy Protection, Disaster Response, Backup\/Restore, and High Availability Design Guide\u003cbr\u003e ◎ Cloud cost optimization, SLA setting, and operational cost management strategies \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e August 27, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 880 pages | 175*235*36mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158396213 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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