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Replaces abbreviated 'ML Systems' with 'Machine Learning Systems' across volume titles, part openers, frontmatter, summaries, docs, and landing page for consistency with MIT Press submission standards.
31 lines
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31 lines
2.2 KiB
Plaintext
# Author's Note {.unnumbered}
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::: {style="font-style: italic;"}
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Volume II: Advanced Machine Learning Systems addresses the challenges of building machine learning systems at production scale. While foundational texts often focus on single-machine systems, this volume teaches you to build and operate them across distributed infrastructure.
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The transition from single machine to distributed systems represents one of the most significant challenges in ML engineering. Models that train in hours on a single GPU may require weeks without proper parallelization strategies. Inference systems that work perfectly in development may fail catastrophically under production load. This volume addresses these challenges systematically.
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**What This Volume Covers**
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The content progresses through four parts that extend foundational concepts:
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- **Foundations of Scale** establishes the infrastructure, storage, and communication patterns required when ML systems span multiple machines
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- **Distributed Training** develops parallelism strategies, fault tolerance mechanisms, and inference optimization for systems at unprecedented scale
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- **Production Concerns** addresses the real world complexity of edge intelligence, security, privacy, and robust system design
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- **Responsible AI at Scale** explores sustainable computing, beneficial applications, and the emerging frontiers that will shape the future
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**Prerequisites**
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This volume assumes familiarity with foundational concepts including ML workflows, neural network architectures, optimization techniques, and deployment fundamentals. Readers new to ML systems should establish this background first.
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**A Collaborative Effort**
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This volume has been shaped by contributions from students, researchers, and practitioners worldwide. The distributed systems and production challenges covered here reflect hard won lessons from deploying ML at scale. I am grateful to everyone who has shared their expertise to make this knowledge accessible.
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As AI systems grow in capability and impact, understanding how to build them responsibly at scale becomes increasingly important. I hope this volume equips you with both the technical skills and ethical framework to contribute positively to this field.
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— Prof. Vijay Janapa Reddi
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