Atjaunināt sīkdatņu piekrišanu

E-grāmata: Engineering AI Systems: Architecture and DevOps Essentials

5.00/5 (4 ratings by Goodreads)
, , ,
  • Formāts: EPUB+DRM
  • Izdošanas datums: 20-Feb-2025
  • Izdevniecība: Addison Wesley
  • Valoda: eng
  • ISBN-13: 9780138261450
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 45,07 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Engineering AI Systems: Architecture and DevOps EssentialsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 20-Feb-2025
  • Izdevniecība: Addison Wesley
  • Valoda: eng
  • ISBN-13: 9780138261450
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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.

Preface
Acknowledgments

Chapter 1: Introduction
Chapter 2: Software Engineering Background
Chapter 3: AI Background
Chapter 4: Foundation Models
Chapter 5: AI Model Lifecycle
Chapter 6: System Lifecycle
Chapter 7: Reliability
Chapter 8: Performance
Chapter 9: Security
Chapter 10: Privacy and Fairness
Chapter 11: Observability
Chapter 12: The Fraunhofer Case Study: Using a Pretrained Language Model for Tendering
Chapter 13: The ARM Hub Case Study: Chatbots for Small and Medium Size Australian Enterprises
Chapter 14: The Banking Case Study: Predicting Customer Churn in Banks
Chapter 15: The Future of AI Engineering

References
Index

Dr. Len Bass is a seasoned researcher with over 30 years in software architecture and more than a decade in DevOps. He has been teaching DevOps to graduate students for seven years and is the author of a bestselling book on software architecture, along with three books on DevOps. Dr. Qinghua Lu is a principal research scientist at CSIRO's Data61, leading the Software Engineering for AI and Responsible AI science teams. She is a coauthor of Responsible AI: Best Practices for Creating Trustworthy AI Systems (Addison-Wesley, 2024). Prof. Dr. Ingo Weber is a professor at the Technical University of Munich and Director of Digital Transformation and ICT Infrastructure at Fraunhofer-Gesellschaft. He has written numerous publications and textbooks, including DevOps: A Software Architects Perspective and Architecture for Blockchain Applications. Dr. Liming Zhu is a research director at CSIRO's Data61 and is a conjoint professor at University of New South Wales. He contributes to various AI safety and standards committees and has written over 300 papers. He is coauthor of Responsible AI: Best Practices for Creating Trustworthy AI Systems.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97801382614506e.html