Mohammad Reza Mahdiani | Mastering Machine Learning Architecture and Solutions. From Design to Deployment (2026) [PDF, EPUB]
Автор: Mohammad Reza Mahdiani
Издательство: Apress
ISBN: 979-8-8688-2527-9, 979-8868825262
Жанр: Компьютерная литература
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание:Mastering Machine Learning Architecture and Solutions is a comprehensive guide to designing and deploying end-to-end ML systems. Ideal for data scientists, machine learning engineers, and architects, this book bridges theoretical foundations with practical applications to help you navigate the complexities of modern ML development.
The book begins with the exploration of ML architecture, it introduces the core concepts and lifecycle stages necessary for successful implementation. It delves into designing robust data pipelines, emphasizing data cleaning, feature engineering, and scaling techniques to support high-performance ML systems. It further discusses model selection and optimization, covering advanced techniques for hyperparameter tuning and managing imbalanced datasets. Readers are introduced to scalable architectural patterns that ensure adaptability and performance, including modular designs and microservices. Infrastructure considerations, such as leveraging cloud solutions and hardware accelerators, are also examined to optimize costs and resources. It also discusses deployment strategies with detailed guidance on containerization, orchestration, and automation. Post-deployment challenges are addressed through chapters on managing, updating, and monitoring live models. Additional topics include rigorous testing, debugging, and ensuring explainability and fairness in models, critical for building trustworthy systems. The book concludes with insights into future trends and ethical considerations shaping the ML landscape.
In the end, this book provides professionals with the tools to build effective and sustainable ML systems, helping them solve modern AI challenges.
What you will learn:
Gain foundational knowledge of machine learning architecture, lifecycle, and implementation strategies.
How to design robust data pipelines with feature engineering and scaling techniques for high-performance systems.
Explore scalable ML system designs, including modular architectures, microservices, and cloud infrastructure optimization.
Understand deployment, monitoring, and ethical considerations to build trustworthy, adaptable, and cost-efficient ML solutions
Who this book is for:
Data scientists, machine learning engineers, AI professionals, and technical professionals aiming to enhance their expertise in ML system architecture and deployment.
About the Author xv
About the Technical Reviewers xvii
Introduction xix
Chapter 1: Introduction to Machine Learning Architecture 1
Chapter 2: Data Pipeline Design for Machine Learning 41
Chapter 3: Selecting and Optimizing Machine Learning Models 103
Chapter 4: Building Scalable and Modular ML Systems 133
Chapter 5: Infrastructure for Machine Learning Workloads 167
Chapter 6: Deployment Strategies for Machine Learning Models 197
Chapter 7: Managing and Updating ML Models in Production 241
Chapter 8: Testing and Debugging ML Systems 287
Chapter 9: Explainability and Interpretability in ML Models 311
Chapter 10: Future Trends and Challenges in Machine Learning Systems 337
Bibliography 359
Index 365
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
