Liming Zhu – författare

The First Complete Guide to DevOps for Software Architects

 

DevOps promises to accelerate the release of new software features and improve monitoring of systems in production, but its crucial implications for software architects and architecture are often ignored.

 

In DevOps: A Software Architect’s Perspective, three leading architects address these issues head-on. The authors review decisions software architects must make in order to achieve DevOps’ goals and clarify how other DevOps participants are likely to impact the architect’s work. They also provide the organizational, technical, and operational context needed to deploy DevOps more efficiently, and review DevOps’ impact on each development phase. The authors address cross-cutting concerns that link multiple functions, offering practical insights into compliance, performance, reliability, repeatability, and security.

 

This guide demonstrates the authors’ ideas in action with three real-world case studies: datacenter replication for business continuity, management of a continuous deployment pipeline, and migration to a microservice architecture.

 

Comprehensive coverage includes

• Why DevOps can require major changes in both system architecture and IT roles

• How virtualization and the cloud can enable DevOps practices

• Integrating operations and its service lifecycle into DevOps

• Designing new systems to work well with DevOps practices

• Integrating DevOps with agile methods and TDD

• Handling failure detection, upgrade planning, and other key issues

• Managing consistency issues arising from DevOps’ independent deployment models

• Integrating security controls, roles, and audits into DevOps

• Preparing a business plan for DevOps adoption, rollout, and measurement

AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level' to be translated into practice. Conversely, AI/ML researchers often focus on algorithmic solutions that are too 'low-level' to adequately address ethics and responsibility. In this timely, practical guide, pioneering AI practitioners bridge these gaps. The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components, offer concrete and actionable guidance for addressing them, and demonstrate these approaches in three detailed case studies.

Writing for technologists, decision-makers, students, users, and other stake-holders, the topics cover:

Governance mechanisms at industry, organisation, and team levels Development process perspectives, including software engineering best practices for AI System perspectives, including quality attributes, architecture styles, and patterns Techniques for connecting code with data and models, including key tradeoffs Principle-specific techniques for fairness, privacy, and explainability A preview of the future of responsible AI

Master the Engineering of AI Systems: The Essential Guide for Architects and Developers

In today's rapidly evolving world, integrating artificial intelligence (AI) into your systems is no longer optional. Engineering AI Systems: Architecture and DevOps Essentials is a comprehensive guide to mastering the complexities of AI systems engineering. This book combines robust software architecture with cutting-edge DevOps practices to deliver high-quality, reliable, and scalable AI solutions.

Experts Len Bass, Qinghua Lu, Ingo Weber, and Liming Zhu demystify the complexities of engineering AI systems, providing practical strategies and tools for seamlessly incorporating AI in your systems. You will gain a comprehensive understanding of the fundamentals of AI and software engineering and how to combine them to create powerful AI systems. Through real-world case studies, the authors illustrate practical applications and successful implementations of AI in small- to medium-sized enterprises across various industries, and offer actionable strategies for designing, building, and operating AI systems that deliver real business value.

Lifecycle management of AI models, from data preparation to deployment  Best practices in system architecture and DevOps for AI systems System reliability, performance, and security in AI implementations Privacy and fairness in AI systems to build trust and achieve compliance Effective monitoring and observability for AI systems to maintain operational excellence Future trends in AI engineering to stay ahead of the curve

Equip yourself with the tools and understanding to lead your organization's AI initiatives. Whether you are a technical lead, software engineer, or business strategist, this book provides the essential insights you need to successfully engineer AI systems.

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

IA Responsable es un eText práctico y estratégico que aborda uno de los desafíos más críticos del desarrollo tecnológico actual: cómo diseñar, implementar y gestionar sistemas de inteligencia artificial de manera ética y responsable. Ubicado entre lo técnico y lo filosófico, este eText ofrece una guía clara y aplicable para profesionales, equipos de desarrollo y líderes organizacionales que buscan integrar principios de IA responsable en la práctica diaria.

A partir de una síntesis de investigación existente y experiencias del mundo real, este eText presenta un marco estructurado basado en patrones comprobados que ayudan a traducir principios éticos en acciones concretas. Con un enfoque accesible y orientado a la aplicación, el contenido permite a los lectores navegar los desafíos éticos de la IA y tomar decisiones informadas, preparando tanto a individuos como a organizaciones para construir soluciones más responsables en un entorno tecnológico en constante evolución.

Enfoque práctico para aplicar principios de IA responsable Equilibrio entre perspectivas técnicas y éticas Marco estructurado basado en patrones probados Orientación dirigida a profesionales de IA y equipos de desarrollo Relevante para líderes organizacionales y toma de decisiones estratégicas Síntesis de investigación existente y mejores prácticas del sector Enfoque en la implementación real, no solo en principios teóricos Cobertura de gobernanza de IA y gestión de riesgos éticos Contenido accesible para distintos niveles de experiencia Preparación para afrontar desafíos futuros en el desarrollo de IA

IA Responsable es un eText práctico y estratégico que aborda uno de los desafíos más críticos del desarrollo tecnológico actual: cómo diseñar, implementar y gestionar sistemas de inteligencia artificial de manera ética y responsable. Ubicado entre lo técnico y lo filosófico, este eText ofrece una guía clara y aplicable para profesionales, equipos de desarrollo y líderes organizacionales que buscan integrar principios de IA responsable en la práctica diaria.

A partir de una síntesis de investigación existente y experiencias del mundo real, este eText presenta un marco estructurado basado en patrones comprobados que ayudan a traducir principios éticos en acciones concretas. Con un enfoque accesible y orientado a la aplicación, el contenido permite a los lectores navegar los desafíos éticos de la IA y tomar decisiones informadas, preparando tanto a individuos como a organizaciones para construir soluciones más responsables en un entorno tecnológico en constante evolución.

Enfoque práctico para aplicar principios de IA responsable Equilibrio entre perspectivas técnicas y éticas Marco estructurado basado en patrones probados Orientación dirigida a profesionales de IA y equipos de desarrollo Relevante para líderes organizacionales y toma de decisiones estratégicas Síntesis de investigación existente y mejores prácticas del sector Enfoque en la implementación real, no solo en principios teóricos Cobertura de gobernanza de IA y gestión de riesgos éticos Contenido accesible para distintos niveles de experiencia Preparación para afrontar desafíos futuros en el desarrollo de IA