Название: Introduction to Machine Learning Systems, Vol 1
Автор: Vijay Janapa Reddi
Издательство: The MIT Press
Год: 2027
Страниц: 1005
Язык: английский
Формат: pdf (true)
Размер: 11.6 MB

Machine learning has evolved from a research discipline into an engineering practice. Building systems that learn from data requires more than understanding algorithms—it demands expertise spanning data pipelines, model development, optimization for deployment constraints, and operational practices. This book introduces AI engineering: the discipline of building ML systems that work in the real world. The treatment covers four areas: foundations (system characteristics, development workflows), building (deep learning mathematics, architectures, framework internals), optimization (compression, hardware acceleration, benchmarking), and deployment (serving infrastructure, operations, responsible engineering). The emphasis throughout is on engineering trade-offs and quantitative analysis.

The core engineering questions are therefore conceptual, not API-specific. A capture compiler needs a frontend that identifies graph regions in an eager program, an intermediate representation that separates the captured computation from Python, and a backend that lowers the region to hardware-specific code. It also needs a guard system: the compiled artifact is valid only while assumptions about tensor rank, dtype, layout, and control-flow path remain true. When a guard fails, the runtime must recompile or fall back to eager execution.

Graph breaks mark the boundary where compilation stops applying. Data-dependent Python control flow, unsupported library calls, I/O, custom Python objects, and highly variable shapes all shorten compiled regions. Each break reintroduces dispatch overhead and may require tensors to move between compiled code and the eager runtime. This is why graph-break analysis belongs in performance engineering: the relevant metric is not whether compilation is enabled, but how much of the hot path remains inside long, stable compiled regions.

What You Will Learn:

The book progresses through four stages:

Part I: Foundations—Build your conceptual foundation with mental models that underpin all effective systems work.
Part II: Build—Engineer complete workflows from data pipelines through training infrastructure.
Part III: Optimize—Transform theoretical understanding into systems that run efficiently in resource-constrained environments.
Part IV: Deploy—Navigate serving, operations, and responsible engineering practices.

Prerequisites:

This book assumes:

Programming proficiency in Python with familiarity in NumPy
Mathematics foundations in linear algebra, calculus, and probability at the undergraduate level
Prior ML experience is helpful but not required; Neural Computation provides essential background

Who This Book Is For:
This book is for anyone who wants to understand how Machine Learning systems actually work, from the mathematics that define a neural network to the hardware that executes it and the engineering decisions that make it run efficiently. Most readers will not become ML hardware architects or compiler engineers. Yet every practitioner who builds, trains, optimizes, or deploys a model makes decisions that depend on understanding the system beneath the code. This book provides that systems foundation for undergraduates encountering ML systems for the first time, practicing engineers strengthening their foundations, and researchers who need to reason about performance and deployment.