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Managing AI in the enterprise Klaus Haller succeeding with AI projects and MLOps to build sustainable AI organizations

By: Language: English Publication details: Apress c2022 New YorkDescription: xix, 214 p. ill. 23 cmISBN:
  • 9781484284360
Subject(s): DDC classification:
  • 006.3 HAL
Summary: Delivering AI projects and building an AI organization are two big challenges for enterprises. They determine whether companies succeed or fail in establishing AI and integrating AI into their digital transformation. This book addresses both challenges by bringing together organizational and service design concepts, project management, and testing and quality assurance. It covers crucial, often-overlooked topics such as MLOps, IT risk, security and compliance, and AI ethics. In particular, the book shows how to shape AI projects and the capabilities of an AI line organization in an enterprise. It elaborates critical deliverables and milestones, helping you turn your vision into a corporate reality by efficiently managing and setting goals for data scientists, data engineers, and other IT specialists. For those new to AI or AI in an enterprise setting you will find this book a systematic introduction to the field. You will get the necessary know-how to collaborate with and lead AI specialists and guide them to success. Time-pressured readers will benefit from self-contained sections explaining key topics and providing illustrations for fostering discussions in their next team, project, or management meeting
Item type: English Books
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Holdings
Current library Call number Status Date due Barcode
Anna Centenary Library 3RD FLOOR, A WING 006.3 HAL (Browse shelf(Opens below)) Available 668324

Reprint, 2023

Includes index

Delivering AI projects and building an AI organization are two big challenges for enterprises. They determine whether companies succeed or fail in establishing AI and integrating AI into their digital transformation. This book addresses both challenges by bringing together organizational and service design concepts, project management, and testing and quality assurance. It covers crucial, often-overlooked topics such as MLOps, IT risk, security and compliance, and AI ethics. In particular, the book shows how to shape AI projects and the capabilities of an AI line organization in an enterprise. It elaborates critical deliverables and milestones, helping you turn your vision into a corporate reality by efficiently managing and setting goals for data scientists, data engineers, and other IT specialists. For those new to AI or AI in an enterprise setting you will find this book a systematic introduction to the field. You will get the necessary know-how to collaborate with and lead AI specialists and guide them to success. Time-pressured readers will benefit from self-contained sections explaining key topics and providing illustrations for fostering discussions in their next team, project, or management meeting

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