About this book
Overview
A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models.
Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems.
• Integrates coverage of cutting-edge research, existing tools, and real-world applications
• Provides students and professionals with an engineering view for production-ready machine learning systems
• Proven in the classroom
• Offers supplemental resources including slides, videos, exams, and further readings
Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems.
• Integrates coverage of cutting-edge research, existing tools, and real-world applications
• Provides students and professionals with an engineering view for production-ready machine learning systems
• Proven in the classroom
• Offers supplemental resources including slides, videos, exams, and further readings
