{"product_id":"operating-ai-9781119833192","title":"Operating AI","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA holistic and real-world approach to operationalizing artificial intelligence in your company InOperating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including keyareas such as; data mesh, data fabric,aspects ofsecurity,data privacy,data rights and IPR related to data and AI models.    In the book, you'll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI)The importance of efficient and reproduceable data pipelines, including how to manage your company's dataAn operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI\/CD\/CT techniques, that generates value in the real worldKey competences and toolsets in AI development, deployment and operationsWhat to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real worldand not just the labOperating AIis a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xii\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Balancing the AI Investment 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining AI and Related Concepts 3\u003c\/p\u003e \u003cp\u003eOperational Readiness and Why It Matters 8\u003c\/p\u003e \u003cp\u003eApplying an Operational Mind- set from the Start 12\u003c\/p\u003e \u003cp\u003eThe Operational Challenge 15\u003c\/p\u003e \u003cp\u003eStrategy, People, and Technology Considerations 19\u003c\/p\u003e \u003cp\u003eStrategic Success Factors in Operating AI 20\u003c\/p\u003e \u003cp\u003ePeople and Mind- sets 23\u003c\/p\u003e \u003cp\u003eThe Technology Perspective 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Data Engineering Focused on AI 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnow Your Data 32\u003c\/p\u003e \u003cp\u003eKnow the Data Structure 32\u003c\/p\u003e \u003cp\u003eKnow the Data Records 34\u003c\/p\u003e \u003cp\u003eKnow the Business Data Oddities 35\u003c\/p\u003e \u003cp\u003eKnow the Data Origin 36\u003c\/p\u003e \u003cp\u003eKnow the Data Collection Scope 37\u003c\/p\u003e \u003cp\u003eThe Data Pipeline 38\u003c\/p\u003e \u003cp\u003eTypes of Data Pipeline Solutions 41\u003c\/p\u003e \u003cp\u003eData Quality in Data Pipelines 44\u003c\/p\u003e \u003cp\u003eThe Data Quality Approach in AI\/ML 45\u003c\/p\u003e \u003cp\u003eScaling Data for AI 49\u003c\/p\u003e \u003cp\u003eKey Capabilities for Scaling Data 51\u003c\/p\u003e \u003cp\u003eIntroducing a Data Mesh 53\u003c\/p\u003e \u003cp\u003eWhen You Have No Data 55\u003c\/p\u003e \u003cp\u003eThe Role of a Data Fabric 56\u003c\/p\u003e \u003cp\u003eWhy a Data Fabric Matters in AI\/ML 58\u003c\/p\u003e \u003cp\u003eKey Competences and Skillsets in Data Engineering 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Embracing MLOps 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMLOps as a Concept 72\u003c\/p\u003e \u003cp\u003eFrom ML Models to ML Pipelines 76\u003c\/p\u003e \u003cp\u003eThe ML Pipeline 78\u003c\/p\u003e \u003cp\u003eAdopt a Continuous Learning Approach 84\u003c\/p\u003e \u003cp\u003eThe Maturity of Your AI\/ML Capability 86\u003c\/p\u003e \u003cp\u003eLevel 0— Model Focus and No MLOps 88\u003c\/p\u003e \u003cp\u003eLevel 1— Pipelines Rather than Models 89\u003c\/p\u003e \u003cp\u003eLevel 2— Leveraging Continuous Learning 90\u003c\/p\u003e \u003cp\u003eThe Model Training Environment 91\u003c\/p\u003e \u003cp\u003eEnabling ML Experimentation 92\u003c\/p\u003e \u003cp\u003eUsing a Simulator for Model Training 94\u003c\/p\u003e \u003cp\u003eEnvironmental Impact of Training AI Models 96\u003c\/p\u003e \u003cp\u003eConsidering the AI\/ML Functional Technology Stack 97\u003c\/p\u003e \u003cp\u003eKey Competences and Toolsets in MLOps 103\u003c\/p\u003e \u003cp\u003eClarifying Similarities and Differences 106\u003c\/p\u003e \u003cp\u003eMLOps Toolsets 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Deployment with AI Operations in Mind 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModel Serving in Practice 117\u003c\/p\u003e \u003cp\u003eFeature Stores 118\u003c\/p\u003e \u003cp\u003eDeploying, Serving, and Inferencing Models at Scale 121\u003c\/p\u003e \u003cp\u003eThe ML Inference Pipeline 123\u003c\/p\u003e \u003cp\u003eModel Serving Architecture Components 125\u003c\/p\u003e \u003cp\u003eConsiderations Regarding Toolsets for Model Serving 129\u003c\/p\u003e \u003cp\u003eThe Industrialization of AI 129\u003c\/p\u003e \u003cp\u003eThe Importance of a Cultural Shift 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Operating AI Is Different from Operating Software 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModel Monitoring 144\u003c\/p\u003e \u003cp\u003eEnsuring Efficient ML Model Monitoring 145\u003c\/p\u003e \u003cp\u003eModel Scoring in Production 146\u003c\/p\u003e \u003cp\u003eRetraining in Production Using Continuous Training 151\u003c\/p\u003e \u003cp\u003eData Aspects Related to Model Retraining 155\u003c\/p\u003e \u003cp\u003eUnderstanding Different Retraining Techniques 156\u003c\/p\u003e \u003cp\u003eDeployment after Retraining 159\u003c\/p\u003e \u003cp\u003eDisadvantages of Retraining Models Frequently 159\u003c\/p\u003e \u003cp\u003eDiagnosing and Managing Model Performance Issues in Operations 161\u003c\/p\u003e \u003cp\u003eIssues with Data Processing 162\u003c\/p\u003e \u003cp\u003eIssues with Data Schema Change 163\u003c\/p\u003e \u003cp\u003eData Loss at the Source 165\u003c\/p\u003e \u003cp\u003eModels Are Broken Upstream 166\u003c\/p\u003e \u003cp\u003eMonitoring Data Quality and Integrity 167\u003c\/p\u003e \u003cp\u003eMonitoring the Model Calls 167\u003c\/p\u003e \u003cp\u003eMonitoring the Data Schema 168\u003c\/p\u003e \u003cp\u003eDetecting Any Missing Data 168\u003c\/p\u003e \u003cp\u003eValidating the Feature Values 169\u003c\/p\u003e \u003cp\u003eMonitor the Feature Processing 170\u003c\/p\u003e \u003cp\u003eModel Monitoring for Stakeholders 171\u003c\/p\u003e \u003cp\u003eEnsuring Stakeholder Collaboration for Model Success 173\u003c\/p\u003e \u003cp\u003eToolsets for Model Monitoring in Production 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 AI Is All About Trust 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnonymizing Data 182\u003c\/p\u003e \u003cp\u003eData Anonymization Techniques 185\u003c\/p\u003e \u003cp\u003ePros and Cons of Data Anonymization 187\u003c\/p\u003e \u003cp\u003eExplainable AI 189\u003c\/p\u003e \u003cp\u003eComplex AI Models Are Harder to Understand 190\u003c\/p\u003e \u003cp\u003eWhat Is Interpretability? 191\u003c\/p\u003e \u003cp\u003eThe Need for Interpretability in Different Phases 192\u003c\/p\u003e \u003cp\u003eReducing Bias in Practice 194\u003c\/p\u003e \u003cp\u003eRights to the Data and AI Models 199\u003c\/p\u003e \u003cp\u003eData Ownership 200\u003c\/p\u003e \u003cp\u003eWho Owns What in a Trained AI Model? 202\u003c\/p\u003e \u003cp\u003eBalancing the IP Approach for AI Models 205\u003c\/p\u003e \u003cp\u003eThe Role of AI Model Training 206\u003c\/p\u003e \u003cp\u003eAddressing IP Ownership in AI Results 207\u003c\/p\u003e \u003cp\u003eLegal Aspects of AI Techniques 208\u003c\/p\u003e \u003cp\u003eOperational Governance of Data and AI 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Achieving Business Value from AI 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Challenge of Leveraging Value from AI 216\u003c\/p\u003e \u003cp\u003eProductivity 216\u003c\/p\u003e \u003cp\u003eReliability 217\u003c\/p\u003e \u003cp\u003eRisk 218\u003c\/p\u003e \u003cp\u003ePeople 219\u003c\/p\u003e \u003cp\u003eTop Management and AI Business Realization 219\u003c\/p\u003e \u003cp\u003eMeasuring AI Business Value 223\u003c\/p\u003e \u003cp\u003eMeasuring AI Value in Nonrevenue Terms 227\u003c\/p\u003e \u003cp\u003eOperating Different AI Business Models 229\u003c\/p\u003e \u003cp\u003eOperating Artificial Intelligence as a Service 230\u003c\/p\u003e \u003cp\u003eOperating Embedded AI Solutions 236\u003c\/p\u003e \u003cp\u003eOperating a Hybrid AI Business Model 239\u003c\/p\u003e \u003cp\u003eIndex 241\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407165956439,"sku":"9781119833192","price":24.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119833192.jpg?v=1730498402","url":"https:\/\/bookcurl.com\/products\/operating-ai-9781119833192","provider":"Book Curl","version":"1.0","type":"link"}