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Managing AI in the enterprise = succ...

Haller, Klaus.

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Managing AI in the enterprise = succeeding with AI projects and MLops to build sustainable AI organizations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Managing AI in the enterprise/ by Klaus Haller.
Reminder of title:	succeeding with AI projects and MLops to build sustainable AI organizations /
Author:	Haller, Klaus.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xix, 214 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Why Organizations Invest in AI -- 2. Structuring and Delivering AI Projects -- 3. Quality Assurance in and for AI -- 4. Ethics, Regulations, and Explainability -- 5. Building an AI Delivery Organization -- 6. AI & Data Management Architectures -- 7. Securing & Protecting AI Environments -- 8. Looking Forward.
Contained By:	Springer Nature eBook
Subject:	Business - Data processing. -
Online resource:	https://doi.org/10.1007/978-1-4842-7824-6
ISBN:	9781484278246

Managing AI in the enterprise = succeeding with AI projects and MLops to build sustainable AI organizations /
Haller, Klaus.

Managing AI in the enterprisesucceeding with AI projects and MLops to build sustainable AI organizations /[electronic resource] :by Klaus Haller. - Berkeley, CA :Apress :2022. - xix, 214 p. :ill., digital ;24 cm.

1. Why Organizations Invest in AI -- 2. Structuring and Delivering AI Projects -- 3. Quality Assurance in and for AI -- 4. Ethics, Regulations, and Explainability -- 5. Building an AI Delivery Organization -- 6. AI & Data Management Architectures -- 7. Securing & Protecting AI Environments -- 8. Looking Forward.

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. Reading this book helps you to better sell the business benefits from your AI initiatives and build your skills around scoping and delivering AI projects. You will be better able to work through critical aspects such as quality assurance, security, and ethics when building AI solutions in your organization. What You Will Learn Clarify the benefits of your AI initiatives and sell them to senior managers Scope and manage AI projects in your organization Set up quality assurance and testing for AI models and their integration in complex software solutions Shape and manage an AI delivery organization, thereby mastering ML Ops Understand and formulate requirements for the underlying data management infrastructure Handle AI-related IT security, compliance, and risk topics and understand relevant AI ethics aspects.

ISBN: 9781484278246

Standard No.: 10.1007/978-1-4842-7824-6doiSubjects--Topical Terms:

527441
Business
--Data processing.

LC Class. No.: HF5548.2 / .H35 2022

Dewey Class. No.: 658.0563

Managing AI in the enterprise = succeeding with AI projects and MLops to build sustainable AI organizations /
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505 0 $a 1. Why Organizations Invest in AI -- 2. Structuring and Delivering AI Projects -- 3. Quality Assurance in and for AI -- 4. Ethics, Regulations, and Explainability -- 5. Building an AI Delivery Organization -- 6. AI & Data Management Architectures -- 7. Securing & Protecting AI Environments -- 8. Looking Forward.
520 $a 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. Reading this book helps you to better sell the business benefits from your AI initiatives and build your skills around scoping and delivering AI projects. You will be better able to work through critical aspects such as quality assurance, security, and ethics when building AI solutions in your organization. What You Will Learn Clarify the benefits of your AI initiatives and sell them to senior managers Scope and manage AI projects in your organization Set up quality assurance and testing for AI models and their integration in complex software solutions Shape and manage an AI delivery organization, thereby mastering ML Ops Understand and formulate requirements for the underlying data management infrastructure Handle AI-related IT security, compliance, and risk topics and understand relevant AI ethics aspects.
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