SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKS Introduction What is AI? AI Epoch's: Waves of Compute AI Hype Cycle - Current and Emerging Technologies AI - End-To-End (E2E) Process - Turning Data into Actionable Insights Microsoft Azure - AI E2E Platform AI Development Operations (DevOps) Loop for Data Science AI -Performance and Computational Notations AI for Greater Good - Solving Humanity and Societal Challenges References Standard Processes and Frameworks Digital Transformation Digital Feedback Loop Insights Value Chain The CRISP-DM Process Building Blocks of AI - Major Components of AI AI Reference Architectures References SECTION II - DATA SOURCES AND ENGINEERING TOOLS Data - Call for Democratization Call for Action The Last Mile - Constrained Compute Devices AND "AI Chasm" References Machine Learning Frameworks and Device Engineering Machine Learning Device Deployments xRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) Framework Circular Buffers AI Democratization - "Crossing the Chasm" References Device Software and Hardware Engineering Tools Software Engineering Tools Hardware and Engineering Tools Libraries References SECTION III - MODEL DEVELOPMENT AND DEPLOYMENT Supervised Models Decision Trees XGBoost Random Forrest Naïve Bayesian Linear Regression Kalman Filter References Unsupervised Models Hierarchical Clustering K-Means Clustering References SECTION IV - DEMOCRATIZATION AND FUTURE OF AI National Strategies National Technology Strategies for Serving People The United Nations AI Technology Strategy The role of the UN AI in the Hands of People References Future Democratization of Artificial Intelligence for the Future of Humanity Dedication Acknowledgement Preface Appendix Index.