Description
PRE-ORDER NOW - Published: 05/03/2026
The book offers a comprehensive blueprint for using AI, IoT, and advanced analytics to achieve measurable sustainability outcomes across industries. It begins with a deep dive into AI basics, including machine learning, deep learning, generative AI, and large language models such as OpenAI's GPT, LLaMA, and DistilBERT. The book addresses the challenges of training Environmental, Social, and Governance (ESG)-focused models, introduces fast language models for edge applications, and explores benchmarks like MMLU, GPQA, and MGSM. Step-by-step tutorials guide readers through developing multi-label ESG IoT Edge models, fine-tuning neural networks, and implementing Retrieval-Augmented Generation and Hierarchical Navigable Small World search patterns. Application oriented chapters highlight IoT-enabled GHG monitoring-including methane and rumination tracking with Class 10 veterinary sensors-feed and water optimization, and AI in disconnected networks for remote agriculture. Real-world case studies cover automation in agriculture and dairy, AI-powered veterinary assistants, market outlook summarization, and agentic AI architectures for ESG intelligence. Combining practical implementation - codes, training pipelines, and deployment guides - with strategic ESG insights, the book provides a cross-disciplinary framework for innovators, policymakers, and business leaders committed to building transparent, scalable, and data-driven ESG platforms aligned with the UN Sustainable Development Goals.Binding: Hardback