{"product_id":"data-centric-engineering-from-sensors-to-decisions-9798295455995","title":"Data-Centric Engineering: From Sensors to Decisions","description":"\u003cp\u003e • Author(s): Ant\u003cbr\u003e • Publisher: Dr. Ant\u003cbr\u003e • Publisher Imprint: Dr. Ant\u003cbr\u003e • BISAC: Artificial Intelligence - Generative AI\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis book explores the transformation of engineering through the integration of data science and machine learning, emphasizing a practical, hands-on approach for students and professionals. It addresses the shift from traditional, intuition-based engineering to a data-centric paradigm, where sensors and digital systems generate vast streams of information that drive innovation, efficiency, and safety.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eFoundations and Data Characteristics: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThe text begins by outlining the evolving landscape of engineering data, highlighting the proliferation of sensors in infrastructure, vehicles, and machinery. It discusses the unique challenges of engineering data, such as its temporal and spatial nature, heterogeneity, multimodality, noise, uncertainty, missing values, and the sheer scale and volume encountered in real-world applications.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eSignal Processing and Feature Engineering: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eA core focus is on transforming raw sensor data into actionable insights. The book details foundational signal processing techniques (time and frequency domain analysis, filtering, wavelet transforms) and the importance of feature engineering-extracting meaningful variables that capture system behavior. It covers statistical, temporal, frequency-based, and domain-specific features, emphasizing their role in improving model performance and interpretability\u003c\/strong\u003e\u003cstrong\u003e1\u003c\/strong\u003e\u003cstrong\u003e.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eData Quality, Pipelines, and Labeling: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eEnsuring data quality is presented as a critical engineering discipline. The book covers strategies for outlier detection, handling missing data, validation, and monitoring. It also addresses the art of data labeling, discussing labeling strategies, tools, quality control, and the challenges of annotating complex engineering datasets.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBuilding Production-Ready ML Pipelines: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThe text provides a comprehensive guide to designing robust, scalable, and maintainable machine learning pipelines. It covers data ingestion, real-time vs. batch processing, automated cleaning, feature engineering, model training, evaluation, deployment (batch, real-time, edge), and continuous monitoring for drift and performance degradation. Emphasis is placed on reproducibility, version control, and the use of MLOps practices\u003c\/strong\u003e\u003cstrong\u003e1\u003c\/strong\u003e\u003cstrong\u003e.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCase Studies and Advanced Topics: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePractical case studies, such as structural health monitoring, illustrate the end-to-end application of these concepts. Advanced topics include explainable AI, reinforcement learning, federated learning, simulation, and digital twins-showing how AI augments engineering decision-making and system optimization. \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBest Practices and Cultural Shift: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThe book concludes by advocating for a data-driven engineering culture, ethical considerations, continuous learning, and thorough documentation. It encourages engineers to embrace data as a strategic asset, fostering collaboration between domain experts and data scientists to drive innovation and ensure the reliability and safety of engineered systems.\u003c\/strong\u003e\u003c\/p\u003e","brand":"Dr. Ant","offers":[{"title":"Paperback","offer_id":46861978534039,"sku":"9798295455995","price":2362.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798295455995.webp?v=1769964266","url":"https:\/\/atlanticbooks.com\/products\/data-centric-engineering-from-sensors-to-decisions-9798295455995","provider":"Atlantic Books","version":"1.0","type":"link"}