Klaus Haller - Managing AI in the Enterprise: Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations
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- Book:Managing AI in the Enterprise: Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations
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Managing AI in the Enterprise: Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations: summary, description and annotation
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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
Who This Book Is For
Experienced IT managers managing data scientists or who want to get involved in managing AI projects, data scientists and other tech professionals who want to progress toward taking on leadership roles in their organizations AI initiatives and who aim to structure AI projects and AI organizations, any line manager and project manager involved in AI projects or in collaborating with AI teams
Klaus Haller: author's other books
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This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.
The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.
To my family.
Artificial intelligence (AI) is what the old west was in the 19th century and the moon in the 1960s: the new frontier. It inspires entrepreneurs, business strategists, and is the hope of CEOs, CFOs, or COOs. It lures ambitious engineers and experienced managers wanting to be part of this big revolution. They all share a passion the passion for finding their unique role and contributing their experience to help AI initiatives succeed. This book paves the road for data scientists, CIOs and senior managers, agile leaders, and project or program managers to successfully run artificial intelligence initiatives. The focus is neither on the glamorous, glittery world of corporate business strategies nor on the world of math. It is on how to connect these two worlds. The book helps you manage an AI Delivery Organization and AI Operations (Machine Learning/ML Ops) with highly specialized data scientists such that your organization provides the AI capabilities the top management and business strategists need for transforming the organization. It helps to master the project and technology management and the organizational challenges of AI.
The challenge is, against popular belief, not finding qualified data scientists. Universities do a great job educating them. The challenge is to form a team and an organization that delivers artificial intelligence solutions in a corporate setting, to integrate the solutions in the IT application landscape, and to run and maintain them. The field is young, so there is a shortage of managers and operations engineers in artificial intelligence with five or ten years of corporate experience. Today, ambitious IT professionals have an excellent chance to fill any of these roles. If you want to deepen your know-how about enterprise AI, that is, about all the hard work it takes to make AI work in an organization, this book is your key to the new frontier of artificial intelligence in the enterprise.
Looking from a strategic perspective, how do organizations benefit from AI? What is the essence of a data-driven company and how does AI help? (Chapter )
How do you successfully deliver AI projects, covering everything from scoping to AI models and integrating them into the overall IT landscape? (Chapter )
Which quality assurance measures help validate the AI models accuracy and their integration in the overall IT landscape? (Chapter )
How do ethical discussions and laws and regulations impact the work of AI specialists? (Chapter )
How can you move from projects to a stable service delivery organization and a successful team? (Chapter )
What are the technical options to store data and deliver the data to the training environments for data scientists or the production systems? (Chapter )
How can organizations protect data, information, AI models, and system environments? These are all valuable assets. Securing them is essential. (Chapter )
The Enterprise AI House Structuring the World of AI And This Book
Rather than discussing all existing theoretical ideas, academic literature, various schools of thought, statistical or mathematical algorithms, and piling footnotes and references, this book compiles and presents concepts and best practices, guiding managers through the most critical decisions. It helps to structure and to organize the work and to guarantee that your initiative stays on track. The aim is always to help readers excel in their careers by providing information they do not get easily elsewhere.
At the end of this introduction, I wish you an inspirational read. Congratulations on starting your journey to the new frontier of enterprise AI. I look forward to your feedback on how your organization and you as a person succeeded and grew further based on the know-how I share with you in this book.
Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484278239. For more detailed information, please visit http://www.apress.com/source-code.
I want to thank my family for their ongoing support. I could not have written this book without the possibility of spending many evenings and weekends preparing my material and without being able to retreat for some weeks when finalizing the book.
My editors, Jonathan Gennick and Jill Balzano from Apress, were crucial for turning my vision of a book into the concrete book you hold right now in your hands. I appreciated Jonathans focus on getting an easy-to-read and helpful book. Both showed great flexibility when I delivered my book chapters earlier than planned.
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