PDF Introduction to Machine Learning Systems, Vol 1

2026 | English | ISBN: NA, ASIN: NA | True PDF | 1005 pages​

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.

 

PDF  Machine Learning Systems at Scale, Vol 2