Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - Chip Huyen

Descriere YEO:

Designing Machine Learning Systems: An - Disponibil la libris.ro

Pe YEO găsești Designing Machine Learning Systems: An de la Chip Huyen, în categoria Computers.

Indiferent de nevoile tale, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - Chip Huyen din categoria Computers îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.

Preț: 368.23 Lei

Caracteristicile produsului Designing Machine Learning Systems: An

• Brand: Chip Huyen
• Categoria: Computers
• Magazin: libris.ro
• Ultima actualizare: 07-03-2025 01:38:19

Comandă Designing Machine Learning Systems: An Online, Simplu și Rapid

Prin intermediul platformei YEO, poți comanda Designing Machine Learning Systems: An de la libris.ro rapid și în siguranță. Bucură-te de o experiență de cumpărături online optimizată și descoperă cele mai bune oferte actualizate constant.

Descriere magazin:
Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. You\'ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure