Distributed Machine Learning Systems

Philosophy
The core philosophy of this book is to bridge the gap between theory and practice. Machine learning is not merely a collection of algorithms; it is an engineering discipline. Similarly, distributed systems are not just theoretical models but the real-world infrastructure that powers modern technology.
Key Features
1. Structured Progression: The book follows a carefully curated path from fundamental principles to advanced, state-of-the-art topics, ensuring a smooth learning curve for beginners while offering substantial depth for advanced learners.
2. Framework-Agnostic Concepts with Practical Implementations: Core principles are taught in a general way, but are always demonstrated using popular, industry-standard frameworks like TensorFlow, PyTorch, Horovod, and Ray.
3. Complete Capstone Project: The final chapter provides a complete, step-by-step walkthrough of a real-world DIY project, including fully-explained, working code, to consolidate learning and provide a portfolio-worthy accomplishment.
4. Focus on the "Why": The book emphasizes not just how to use a tool or technique, but why it was designed that way and what trade-offs it embodies, fostering deeper and more durable knowledge.
5. Up-to-Date Content: Includes modern topics such as training Large Language Models (LLMs), Federated Learning, and MLOps for distributed environments.
To Whom This Book Is For
This book is primarily intended for:
1. Undergraduate Students (B.Tech/B.E.): Senior-level students in Computer Science, Information Technology, AI & Machine Learning, and Data Science who have a basic understanding of machine learning and are taking a course on distributed systems, parallel computing, or advanced ML.
2. Graduate Students (M.Tech/M.S./Ph.D.): Students specializing in Machine Learning, AI, or High-Performance Computing who need a comprehensive resource for their research and coursework.
3. Machine Learning Engineers & Data Scientists: Professionals in the industry who need to scale their models and workflows from a single machine to a distributed cluster.
4. Software Engineers & System Architects: Developers and architects who are tasked with building the infrastructure and platforms to support large-scale machine learning applications.

Rapidgator
9nsdu.7z.html
DDownload
9nsdu.7z
FreeDL
9nsdu.7z.html
AlfaFile
9nsdu.7z