{"product_id":"introduction-to-machine-learning-systems-isbn-9780262058889","title":"Introduction to Machine Learning Systems","description":"\u003cb\u003eAn 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.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThis 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, \u003ci\u003eIntroduction to Machine Learning Systems\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e Provides end-to-end coverage of the machine learning systems lifecycle \u003c\/li\u003e\n\u003cli\u003e Emphasizes benchmarking, performance, and empirical rigor \u003c\/li\u003e\n\u003cli\u003e Offers rich pedagogy including learning objectives and self-check questions  \u003c\/li\u003e\n\u003cli\u003e Integrates with open-source tooling and real system case studies \u003c\/li\u003e\n\u003cli\u003e Based on the author’s popular Harvard course and class tested by thousands of students worldwide \u003c\/li\u003e\n\u003cli\u003e Features extensive supplemental resources including labs\u003c\/li\u003e\n\u003c\/ul\u003e | \u003cb\u003eVijay Janapa Reddi\u003c\/b\u003e is the Gordon McKay Professor of Electrical Engineering at Harvard University, where his research focuses on the intersection of computer architecture, machine learning systems, and autonomous agents. He is Vice President and cofounder of MLCommons, a nonprofit organization dedicated to accelerating machine learning innovation, and created the Tiny Machine Learning edX series, a global MOOC that has trained over 100,000 learners.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":48759469441253,"sku":"NP9780262058889","price":100.0,"currency_code":"USD","in_stock":false}],"url":"https:\/\/k12savings.com\/products\/introduction-to-machine-learning-systems-isbn-9780262058889","provider":"K12savings","version":"1.0","type":"link"}