Chandrasekar Vuppalapati graduated from San Jose State University Masters Program, specializing Software Engineering, and completed his Master of Business Administration from Santa Clara University, Santa Clara, California, USA. He is a Software IT Executive and Entrepreneur with diverse experience in Software Technologies, Enterprise Software Architectures, Cloud Computing, Data Analytics, Internet of Things (IoT), and Software Product & Program Management. Chandra has held engineering architectures and product leadership roles at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies, a Bell Laboratories Company. He teaches Software Engineering, Large Scale Analytics, Data Science, Mobile Technologies, Cloud Technologies, and Web & Data Mining for Masters program in San Jose State University. Chandra has also held market research, strategy and technology architecture advisory roles in Cisco Systems, Lam Research and performed Principal Investigator role for Valley School of Nursing where he connected Nursing Educators & Students with Virtual Reality technologies. He has authored several international conference papers and published book on Building Enterprise IoT Applications. Chandra has served as Chair in numerous technology and advanced computing conferences such as: IEEE Oxford, UK, IEEE Big Data Services 2017, San Francisco USA, Future of Information and Communication Conference 2018, Singapore and Intelligent Human Systems Integration (IHSI) 2020, Modena, Italy.

• SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKSIntroductionWhat is AI? AI Epoch’s: Waves of ComputeAI Hype Cycle – Current and Emerging TechnologiesAI - End-To-End (E2E) Process – Turning Data into Actionable InsightsMicrosoft Azure - AI E2E PlatformAI Development Operations (DevOps) Loop for Data ScienceAI –Performance and Computational NotationsAI for Greater Good – Solving Humanity and Societal ChallengesReferencesStandard Processes and FrameworksDigital TransformationDigital Feedback LoopInsights Value ChainThe CRISP-DM ProcessBuilding Blocks of AI - Major Components of AIAI Reference ArchitecturesReferencesSECTION II - DATA SOURCES AND ENGINEERING TOOLSData – Call for Democratization Call for ActionThe Last Mile - Constrained Compute Devices AND "AI Chasm"ReferencesMachine Learning Frameworks and Device EngineeringMachine Learning Device DeploymentsxRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) FrameworkCircular BuffersAI Democratization – "Crossing the Chasm"ReferencesDevice Software and Hardware Engineering ToolsSoftware Engineering ToolsHardware and Engineering ToolsLibrariesReferencesSECTION III - MODEL DEVELOPMENT AND DEPLOYMENTSupervised Models Decision TreesXGBoostRandom ForrestNaïve BayesianLinear RegressionKalman FilterReferencesUnsupervised ModelsHierarchical ClusteringK-Means ClusteringReferencesSECTION IV - DEMOCRATIZATION AND FUTURE OF AINational Strategies National Technology Strategies for Serving PeopleThe United Nations AI Technology Strategy The role of the UN AI in the Hands of People ReferencesFuture Democratization of Artificial Intelligence for the Future of Humanity DedicationAcknowledgementPrefaceAppendix Index

Chandrasekar Vuppalapati graduated from San Jose State University Masters Program, specializing Software Engineering, and completed his Master of Business Administration from Santa Clara University, Santa Clara, California, USA. He is a Software IT Executive and Entrepreneur with diverse experience in Software Technologies, Enterprise Software Architectures, Cloud Computing, Data Analytics, Internet of Things (IoT), and Software Product & Program Management. Chandra has held engineering architectures and product leadership roles at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies, a Bell Laboratories Company. He teaches Software Engineering, Large Scale Analytics, Data Science, Mobile Technologies, Cloud Technologies, and Web & Data Mining for Masters program in San Jose State University. Chandra has also held market research, strategy and technology architecture advisory roles in Cisco Systems, Lam Research and performed Principal Investigator role for Valley School of Nursing where he connected Nursing Educators & Students with Virtual Reality technologies. He has authored several international conference papers and published book on Building Enterprise IoT Applications. Chandra has served as Chair in numerous technology and advanced computing conferences such as: IEEE Oxford, UK, IEEE Big Data Services 2017, San Francisco USA, Future of Information and Communication Conference 2018, Singapore and Intelligent Human Systems Integration (IHSI) 2020, Modena, Italy.

• SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKSIntroductionWhat is AI? AI Epoch’s: Waves of ComputeAI Hype Cycle – Current and Emerging TechnologiesAI - End-To-End (E2E) Process – Turning Data into Actionable InsightsMicrosoft Azure - AI E2E PlatformAI Development Operations (DevOps) Loop for Data ScienceAI –Performance and Computational NotationsAI for Greater Good – Solving Humanity and Societal ChallengesReferencesStandard Processes and FrameworksDigital TransformationDigital Feedback LoopInsights Value ChainThe CRISP-DM ProcessBuilding Blocks of AI - Major Components of AIAI Reference ArchitecturesReferencesSECTION II - DATA SOURCES AND ENGINEERING TOOLSData – Call for Democratization Call for ActionThe Last Mile - Constrained Compute Devices AND "AI Chasm"ReferencesMachine Learning Frameworks and Device EngineeringMachine Learning Device DeploymentsxRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) FrameworkCircular BuffersAI Democratization – "Crossing the Chasm"ReferencesDevice Software and Hardware Engineering ToolsSoftware Engineering ToolsHardware and Engineering ToolsLibrariesReferencesSECTION III - MODEL DEVELOPMENT AND DEPLOYMENTSupervised Models Decision TreesXGBoostRandom ForrestNaïve BayesianLinear RegressionKalman FilterReferencesUnsupervised ModelsHierarchical ClusteringK-Means ClusteringReferencesSECTION IV - DEMOCRATIZATION AND FUTURE OF AINational Strategies National Technology Strategies for Serving PeopleThe United Nations AI Technology Strategy The role of the UN AI in the Hands of People ReferencesFuture Democratization of Artificial Intelligence for the Future of Humanity DedicationAcknowledgementPrefaceAppendix Index

Chandrasekar Vuppalapati graduated from San Jose State University Masters Program, specializing Software Engineering, and completed his Master of Business Administration from Santa Clara University, Santa Clara, California, USA. He is a Software IT Executive and Entrepreneur with diverse experience in Software Technologies, Enterprise Software Architectures, Cloud Computing, Data Analytics, Internet of Things (IoT), and Software Product & Program Management. Chandra has held engineering architectures and product leadership roles at Microsoft, GE Healthcare, Cisco Systems, St. Jude Medical, and Lucent Technologies, a Bell Laboratories Company. He teaches Software Engineering, Large Scale Analytics, Data Science, Mobile Technologies, Cloud Technologies, and Web & Data Mining for Masters program in San Jose State University. Chandra has also held market research, strategy and technology architecture advisory roles in Cisco Systems, Lam Research and performed Principal Investigator role for Valley School of Nursing where he connected Nursing Educators & Students with Virtual Reality technologies. He has authored several international conference papers and published book on Building Enterprise IoT Applications. Chandra has served as Chair in numerous technology and advanced computing conferences such as: IEEE Oxford, UK, IEEE Big Data Services 2017, San Francisco USA, Future of Information and Communication Conference 2018, Singapore and Intelligent Human Systems Integration (IHSI) 2020, Modena, Italy.

• SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKSIntroductionWhat is AI? AI Epoch’s: Waves of ComputeAI Hype Cycle – Current and Emerging TechnologiesAI - End-To-End (E2E) Process – Turning Data into Actionable InsightsMicrosoft Azure - AI E2E PlatformAI Development Operations (DevOps) Loop for Data ScienceAI –Performance and Computational NotationsAI for Greater Good – Solving Humanity and Societal ChallengesReferencesStandard Processes and FrameworksDigital TransformationDigital Feedback LoopInsights Value ChainThe CRISP-DM ProcessBuilding Blocks of AI - Major Components of AIAI Reference ArchitecturesReferencesSECTION II - DATA SOURCES AND ENGINEERING TOOLSData – Call for Democratization Call for ActionThe Last Mile - Constrained Compute Devices AND "AI Chasm"ReferencesMachine Learning Frameworks and Device EngineeringMachine Learning Device DeploymentsxRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) FrameworkCircular BuffersAI Democratization – "Crossing the Chasm"ReferencesDevice Software and Hardware Engineering ToolsSoftware Engineering ToolsHardware and Engineering ToolsLibrariesReferencesSECTION III - MODEL DEVELOPMENT AND DEPLOYMENTSupervised Models Decision TreesXGBoostRandom ForrestNaïve BayesianLinear RegressionKalman FilterReferencesUnsupervised ModelsHierarchical ClusteringK-Means ClusteringReferencesSECTION IV - DEMOCRATIZATION AND FUTURE OF AINational Strategies National Technology Strategies for Serving PeopleThe United Nations AI Technology Strategy The role of the UN AI in the Hands of People ReferencesFuture Democratization of Artificial Intelligence for the Future of Humanity DedicationAcknowledgementPrefaceAppendix Index