{"product_id":"enterprise-artificial-intelligence-transformation-isbn-9781119665939","title":"Enterprise Artificial Intelligence Transformation","description":"\u003cp\u003e\u003cb\u003eEnterprise Artificial Intelligence Transformation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals.\u003c\/p\u003e \u003cp\u003eAuthor Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, \u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eThe first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e covers a wide range of topics, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eReal-world AI use cases and examples\u003c\/li\u003e \u003cli\u003eMachine learning, deep learning, and slimantic modeling\u003c\/li\u003e \u003cli\u003eRisk management of AI models\u003c\/li\u003e \u003cli\u003eAI strategies for development and expansion\u003c\/li\u003e \u003cli\u003eAI Center of Excellence creating and management\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIf you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in \u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eForeword: Artificial Intelligence and the New Generation of Technology Building Blocks xv\u003c\/p\u003e \u003cp\u003ePrologue: A Guide to This Book xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: \u003c\/b\u003e\u003cb\u003eA Brief Introduction to Artificial Intelligence 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: \u003c\/b\u003e\u003cb\u003eA Revolution in the Making 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Impact of the Four Revolutions 4\u003c\/p\u003e \u003cp\u003eAI Myths and Reality 6\u003c\/p\u003e \u003cp\u003eThe Data and Algorithms Virtuous Cycle 7\u003c\/p\u003e \u003cp\u003eThe Ongoing Revolution – Why Now? 8\u003c\/p\u003e \u003cp\u003eAI: Your Competitive Advantage 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: \u003c\/b\u003e\u003cb\u003eWhat Is AI and How Does It Work? 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Development of Narrow AI 18\u003c\/p\u003e \u003cp\u003eThe First Neural Network 20\u003c\/p\u003e \u003cp\u003eMachine Learning 20\u003c\/p\u003e \u003cp\u003eTypes of Uses for Machine Learning 23\u003c\/p\u003e \u003cp\u003eTypes of Machine Learning Algorithms 24\u003c\/p\u003e \u003cp\u003eSupervised, Unsupervised, and Semisupervised Learning 28\u003c\/p\u003e \u003cp\u003eMaking Data More Useful 32\u003c\/p\u003e \u003cp\u003eSemantic Reasoning 34\u003c\/p\u003e \u003cp\u003eApplications of AI 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: \u003c\/b\u003e\u003cb\u003eArtificial Intelligence In the Enterprise 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: \u003c\/b\u003e\u003cb\u003eAI in E-Commerce and Retail 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDigital Advertising 46\u003c\/p\u003e \u003cp\u003eMarketing and Customer Acquisition 48\u003c\/p\u003e \u003cp\u003eCross-Selling, Up-Selling, and Loyalty 52\u003c\/p\u003e \u003cp\u003eBusiness-to-Business Customer Intelligence 55\u003c\/p\u003e \u003cp\u003eDynamic Pricing and Supply Chain Optimization 57\u003c\/p\u003e \u003cp\u003eDigital Assistants and Customer Engagement 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: \u003c\/b\u003e\u003cb\u003eAI in Financial Services 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnti-Money Laundering 68\u003c\/p\u003e \u003cp\u003eLoans and Credit Risk 71\u003c\/p\u003e \u003cp\u003ePredictive Services and Advice 72\u003c\/p\u003e \u003cp\u003eAlgorithmic and Autonomous Trading 75\u003c\/p\u003e \u003cp\u003eInvestment Research and Market Insights 77\u003c\/p\u003e \u003cp\u003eAutomated Business Operations 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: \u003c\/b\u003e\u003cb\u003eAI in Manufacturing and Energy 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOptimized Plant Operations and Assets Maintenance 88\u003c\/p\u003e \u003cp\u003eAutomated Production Lifecycles 91\u003c\/p\u003e \u003cp\u003eSupply Chain Optimization 91\u003c\/p\u003e \u003cp\u003eInventory Management and Distribution Logistics 93\u003c\/p\u003e \u003cp\u003eElectric Power Forecasting and Demand Response 94\u003c\/p\u003e \u003cp\u003eOil Production 96\u003c\/p\u003e \u003cp\u003eEnergy Trading 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: \u003c\/b\u003e\u003cb\u003eAI in Healthcare 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePharmaceutical Drug Discovery 104\u003c\/p\u003e \u003cp\u003eClinical Trials 105\u003c\/p\u003e \u003cp\u003eDisease Diagnosis 106\u003c\/p\u003e \u003cp\u003ePreparation for Palliative Care 109\u003c\/p\u003e \u003cp\u003eHospital Care 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III: \u003c\/b\u003e\u003cb\u003eBUILDING YOUR ENTERPRISE AI CAPABILITY 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: \u003c\/b\u003e\u003cb\u003eDeveloping an AI Strategy 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGoals of Connected Intelligence Systems 120\u003c\/p\u003e \u003cp\u003eThe Challenges of Implementing AI 122\u003c\/p\u003e \u003cp\u003eAI Strategy Components 126\u003c\/p\u003e \u003cp\u003eSteps to Develop an AI Strategy 127\u003c\/p\u003e \u003cp\u003eSome Assembly Required 129\u003c\/p\u003e \u003cp\u003eCreating an AI Center of Excellence 130\u003c\/p\u003e \u003cp\u003eBuilding an AI Platform 131\u003c\/p\u003e \u003cp\u003eDefining a Data Strategy 132\u003c\/p\u003e \u003cp\u003eMoving Ahead 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: \u003c\/b\u003e\u003cb\u003eThe AI Lifecycle 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Use Cases 138\u003c\/p\u003e \u003cp\u003eCollecting, Assessing, and Remediating Data 143\u003c\/p\u003e \u003cp\u003eData Instrumentation 144\u003c\/p\u003e \u003cp\u003eData Cleansing 145\u003c\/p\u003e \u003cp\u003eData Labeling 146\u003c\/p\u003e \u003cp\u003eFeature Engineering 148\u003c\/p\u003e \u003cp\u003eSelecting and Training a Model 151\u003c\/p\u003e \u003cp\u003eManaging Models 160\u003c\/p\u003e \u003cp\u003eTesting, Deploying, and Activating Models 164\u003c\/p\u003e \u003cp\u003eTesting 164\u003c\/p\u003e \u003cp\u003eGoverning Model Risk 165\u003c\/p\u003e \u003cp\u003eDeploying the Model 166\u003c\/p\u003e \u003cp\u003eActivating the Model 166\u003c\/p\u003e \u003cp\u003eProduction Monitoring 168\u003c\/p\u003e \u003cp\u003eConclusion 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: \u003c\/b\u003e\u003cb\u003eBuilding the Perfect AI Engine 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI Platforms versus AI Applications 172\u003c\/p\u003e \u003cp\u003eWhat AI Platform Architectures Should Do 172\u003c\/p\u003e \u003cp\u003eSome Important Considerations 179\u003c\/p\u003e \u003cp\u003eShould a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? 179\u003c\/p\u003e \u003cp\u003eShould a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? 180\u003c\/p\u003e \u003cp\u003eShould a Business Use Batch or Real-Time Processing? 182\u003c\/p\u003e \u003cp\u003eShould a Business Use Monolithic or Microservices Architecture? 184\u003c\/p\u003e \u003cp\u003eAI Platform Architecture 186\u003c\/p\u003e \u003cp\u003eData Minder 186\u003c\/p\u003e \u003cp\u003eModel Maker 187\u003c\/p\u003e \u003cp\u003eInference Activator 188\u003c\/p\u003e \u003cp\u003ePerformance Manager 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: \u003c\/b\u003e\u003cb\u003eManaging Model Risk 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhen Algorithms Go Wrong 195\u003c\/p\u003e \u003cp\u003eMitigating Model Risk 197\u003c\/p\u003e \u003cp\u003eBefore Modeling 197\u003c\/p\u003e \u003cp\u003eDuring Modeling 199\u003c\/p\u003e \u003cp\u003eAfter Modeling 201\u003c\/p\u003e \u003cp\u003eModel Risk Office 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: \u003c\/b\u003e\u003cb\u003eActivating Organizational Capability 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAligning Stakeholders 214\u003c\/p\u003e \u003cp\u003eOrganizing for Scale 215\u003c\/p\u003e \u003cp\u003eAI Center of Excellence 217\u003c\/p\u003e \u003cp\u003eStandards and Project Governance 218\u003c\/p\u003e \u003cp\u003eCommunity, Knowledge, and Training 220\u003c\/p\u003e \u003cp\u003ePlatform and AI Ecosystem 221\u003c\/p\u003e \u003cp\u003eStructuring Teams for Project Execution 222\u003c\/p\u003e \u003cp\u003eManaging Talent and Hiring 225\u003c\/p\u003e \u003cp\u003eData Literacy, Experimentation, and Data-Driven Decisions 228\u003c\/p\u003e \u003cp\u003eConclusion 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: \u003c\/b\u003e\u003cb\u003eDelving Deeper Into AI Architecture and Modeling 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: \u003c\/b\u003e\u003cb\u003eArchitecture and Technical Patterns 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI Platform Architecture 236\u003c\/p\u003e \u003cp\u003eData Minder 236\u003c\/p\u003e \u003cp\u003eModel Maker 239\u003c\/p\u003e \u003cp\u003eInference Activator 242\u003c\/p\u003e \u003cp\u003ePerformance Manager 244\u003c\/p\u003e \u003cp\u003eTechnical Patterns 244\u003c\/p\u003e \u003cp\u003eIntelligent Virtual Assistant 244\u003c\/p\u003e \u003cp\u003ePersonalization and Recommendation Engines 247\u003c\/p\u003e \u003cp\u003eAnomaly Detection 250\u003c\/p\u003e \u003cp\u003eAmbient Sensing and Physical Control 251\u003c\/p\u003e \u003cp\u003eDigital Workforce 255\u003c\/p\u003e \u003cp\u003eConclusion 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: \u003c\/b\u003e\u003cb\u003eThe AI Modeling Process 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining the Use Case and the AI Task 260\u003c\/p\u003e \u003cp\u003eSelecting the Data Needed 262\u003c\/p\u003e \u003cp\u003eSetting Up the Notebook Environment and Importing Data 264\u003c\/p\u003e \u003cp\u003eCleaning and Preparing the Data 265\u003c\/p\u003e \u003cp\u003eUnderstanding the Data Using Exploratory Data Analysis 268\u003c\/p\u003e \u003cp\u003eFeature Engineering 274\u003c\/p\u003e \u003cp\u003eCreating and Selecting the Optimal Model 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: \u003c\/b\u003e\u003cb\u003eLooking Ahead 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: \u003c\/b\u003e\u003cb\u003eThe Future of Society, Work, and AI 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI and the Future of Society 292\u003c\/p\u003e \u003cp\u003eAI and the Future of Work 294\u003c\/p\u003e \u003cp\u003eRegulating Data and Artificial Intelligence 296\u003c\/p\u003e \u003cp\u003eThe Future of AI: Improving AI Technology 300\u003c\/p\u003e \u003cp\u003eReinforcement Learning 300\u003c\/p\u003e \u003cp\u003eGenerative Adversarial Learning 302\u003c\/p\u003e \u003cp\u003eFederated Learning 303\u003c\/p\u003e \u003cp\u003eNatural Language Processing 304\u003c\/p\u003e \u003cp\u003eCapsule Networks 305\u003c\/p\u003e \u003cp\u003eQuantum Machine Learning 306\u003c\/p\u003e \u003cp\u003eAnd This Is Just the Beginning 307\u003c\/p\u003e \u003cp\u003eFurther Reading 313\u003c\/p\u003e \u003cp\u003eAcknowledgments 317\u003c\/p\u003e \u003cp\u003eAbout the Author 319\u003c\/p\u003e \u003cp\u003eIndex 321\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eRASHED HAQ\u003c\/b\u003e is an AI and robotics technologist. He was recently appointed as the Vice President of Robotics at Cruise, one of the leading autonomous vehicle companies. He was previously the Global Head of AI \u0026amp; Data and Group Vice President at Publicis Sapient. He has spent over 20 years helping companies transform and create sustained competitive advantage through technology and data. Rashed holds advanced degrees in theoretical physics and mathematics. An accomplished author and sought-after speaker, Rashed frequently writes about the practical uses of AI in business.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eBY NOW, WE HAVE\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eseen enough evidence and heard enough success stories to know that artificial intelligence is potentially game changing, especially at scale. Large enterprises have the opportunity to create new lines of revenue, generate deep business insights, streamline the delivery of products and services, and free up considerable manpower. The cost–benefit equation clearly points the way to an AI-driven future. So how can more companies really take advantage of this new technology? \u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e contains the answer.  \u003c\/p\u003e\u003cp\u003eAuthor Rashed Haq has two decades of experience transforming large businesses through technology. Over the course of his career, he has seen firsthand the obstacles that lead to stagnant operating models, even when adopting new technology is the clear best choice. A major roadblock is presented by the technology itself. Of all the complex AI solution options available, what is the optimal architecture that should be assembled that will be best suited to a particular organization, and how can we hire and retain people who know how to (safely) implement them? Change management is another huge hurdle—how can we generate buy-in, not only for the initial investment in AI, but for the iterative processes required to sustain a functioning AI capability over time? (And what are those processes, anyway?)  \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e is a one-of-a-kind book that delves into both the business and technical aspects of AI business transformation. We have the data available, we see that society is heading in the direction of AI and machine learning, and we can envision ourselves as the algorithmic, data-driven market leaders of the future. What we have been missing—up to now—is a clear guide to understanding the issues involved in actually taking that step, making the transition to large-scale artificially intelligent operation. This is that guide.    \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePRAISE FOR ENTERPRISE ARTIFICIAL INTELLIGENCE TRANSFORMATION\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003e\"Given the breadth of opportunities and the importance of a balanced approach to your organization's AI journey, this book provides a critical reference for business leaders on how to think about your company's—as well as your personal—AI plan.\"\u003cbr\u003e \u003cb\u003e—Steve Guggenheimer,\u003c\/b\u003e CVP for Artificial Intelligence, Microsoft \u003c\/p\u003e\u003cp\u003e\"This book is an excellent and much needed guide for leaders transforming their organizations towards an algorithmic enterprise.\"\u003cbr\u003e \u003cb\u003e—Hussein Mehanna,\u003c\/b\u003e Head of Artificial Intelligence, Cruise \u003c\/p\u003e\u003cp\u003e\"Artificial intelligence is more than the mere use of machine learning algorithms, and its successful implementation is equally about building a practical solution that fits into the ecosystem and culture of its end-users. This insightful book explains how to do it—without the hot air.\"\u003cbr\u003e \u003cb\u003e—Aldo Faisal,\u003c\/b\u003e Professor of Artificial Intelligence, Imperial College London  \u003c\/p\u003e\u003cp\u003eACHIEVE, MAINTAIN, AND GROW SUCCESSFUL AND COST-EFFECTIVE AI AT SCALE  \u003c\/p\u003e\u003cp\u003e\u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e is a unique framework for making revolutionary changes to the way your organization works. Artificial intelligence is poised to transform business and, thereby, humanity. We are on the verge of creating algorithmic enterprises capable of leveraging AI to improve business decisions, streamline processes, and develop new product lines. With this new technology at our fingertips, we can create business insights from data that would have been impossible in the pre-AI era.  \u003c\/p\u003e\u003cp\u003eLarge organizations are ready to implement enterprise-wide AI strategies, but for established firms there are significant hurdles to doing so. From change management and culture issues to technical and human resource challenges, AI presents a major organizational upheaval. To help you overcome these hurdles and put in place a solid, scalable AI capability, \u003ci\u003eEnterprise Artificial Intelligence Transformation\u003c\/i\u003e offers a roadmap to understanding and seizing upon the opportunities that AI is opening up.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003erashedhaq.com\/aibook\u003c\/b\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989141012709,"sku":"NP9781119665939","price":39.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119665939.jpg?v=1761782962","url":"https:\/\/k12savings.com\/es\/products\/enterprise-artificial-intelligence-transformation-isbn-9781119665939","provider":"K12savings","version":"1.0","type":"link"}