This book delivers a practical blueprint for applying AI, IoT, and advanced analytics to drive measurable ESG outcomes. It covers machine learning, deep learning, generative AI, and fast language models for edge use, with benchmarks like MMLU, GPQA, and MGSM. Step-by-step guides teach ESG IoT model building, neural network fine-tuning, and RAG implementations. Application chapters span IoT-enabled GHG monitoring, methane/rumination tracking with Class 10 sensors, and AI for remote farming. Real-world case studies and deployment pipelines provide innovators, policymakers, and leaders with a cross-disciplinary framework aligned to UN Sustainable Development Goals.

This book delivers a practical blueprint for applying AI, IoT, and advanced analytics to drive measurable ESG outcomes. It covers machine learning, deep learning, generative AI, and fast language models for edge use, with benchmarks like MMLU, GPQA, and MGSM. Step-by-step guides teach ESG IoT model building, neural network fine-tuning, and RAG implementations. Application chapters span IoT-enabled GHG monitoring, methane/rumination tracking with Class 10 sensors, and AI for remote farming. Real-world case studies and deployment pipelines provide innovators, policymakers, and leaders with a cross-disciplinary framework aligned to UN Sustainable Development Goals.

This book delivers a practical blueprint for applying AI, IoT, and advanced analytics to drive measurable ESG outcomes. It covers machine learning, deep learning, generative AI, and fast language models for edge use, with benchmarks like MMLU, GPQA, and MGSM. Step-by-step guides teach ESG IoT model building, neural network fine-tuning, and RAG implementations. Application chapters span IoT-enabled GHG monitoring, methane/rumination tracking with Class 10 sensors, and AI for remote farming. Real-world case studies and deployment pipelines provide innovators, policymakers, and leaders with a cross-disciplinary framework aligned to UN Sustainable Development Goals.