Name: Introduction to Machine Learning Systems (en Inglés)
SKU: 67164198
Price: 97.26 USD
Availability: InStock
Author: Vijay Janapa Reddi

portada Introduction to Machine Learning Systems (en Inglés)

Introduction to Machine Learning Systems (en Inglés)

Formato

Libro Físico

Editorial

MIT Press

Autor

Vijay Janapa Reddi

Año

2026

Idioma

Inglés

N° páginas

976

Encuadernación

Tapa Dura

Dimensiones

25.4x20.32 cm

ISBN13

9780262058889

Introduction to Machine Learning Systems (en Inglés)

Vijay Janapa Reddi (Autor) · MIT Press · Tapa Dura

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Reseña del libro "Introduction to Machine Learning Systems (en Inglés)"

An innovative textbook that uses a systems approach to teach students and practitioners how to engineer machine learning systems that are reliable, efficient, and scalable in real-world settings.

This groundbreaking textbook provides a comprehensive framework for understanding and engineering machine learning systems, emphasizing the systems perspective required to build effective AI solutions. Unlike resources that focus primarily on algorithms and model architectures, Introduction to Machine Learning Systems integrates engineering principles, system abstractions, and practical techniques to bridge the persistent gap between theoretical foundations and production. It covers the full lifecycle, including systems foundations, data pipelines, training and inference infrastructure, deployment, monitoring, benchmarking, security, privacy, and sustainability. The scope spans edge devices, embedded systems, and large-scale cloud platforms. Following a pedagogical progression that mirrors how expert engineers develop their skills, this learn-by-doing text equips readers to reason about machine learning system architectures and apply enduring engineering principles to build flexible, efficient, and robust machine learning systems.

Provides end-to-end coverage of the machine learning systems lifecycle Emphasizes benchmarking, performance, and empirical rigor Offers rich pedagogy including learning objectives and self-check questions Integrates with open-source tooling and real system case studies Based on the author’s popular Harvard course and class tested by thousands of students worldwide Features extensive supplemental resources including labs