LEADER 04496nam 2200517 450 001 9910522999303321 005 20220905160532.0 010 $a1-4842-7824-0 024 7 $a10.1007/978-1-4842-7824-6 035 $a(MiAaPQ)EBC6838838 035 $a(Au-PeEL)EBL6838838 035 $a(CKB)20275197900041 035 $a(OCoLC)1291314197 035 $a(OCoLC)1290841098 035 $a(OCoLC-P)1290841098 035 $a(CaSebORM)9781484278246 035 $a(EXLCZ)9920275197900041 100 $a20220905d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aManaging AI in the enterprise $esucceeding with AI projects and MLops to build sustainable AI organizations /$fKlaus Haller 210 1$aNew York, New York :$cApress Media LLC,$d[2022] 210 4$dİ2022 215 $a1 online resource (223 pages) 300 $aIncludes index. 311 08$aPrint version: Haller, Klaus Managing AI in the Enterprise Berkeley, CA : Apress L. P.,c2021 9781484278239 327 $a1. 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. 330 $aDelivering 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 organization's 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. 606 $aBusiness$xData processing 606 $aProject management$xTechnological innovations 606 $aArtificial intelligence$xIndustrial applications 615 0$aBusiness$xData processing. 615 0$aProject management$xTechnological innovations. 615 0$aArtificial intelligence$xIndustrial applications. 676 $a658.0563 700 $aHaller$b Klaus$f1939-2011,$01039332 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522999303321 996 $aManaging AI in the Enterprise$92590781 997 $aUNINA