Mr. Navadeep Vempati

Smart Predictive Systems : Integrating IoT, Data Engineering and Machine Learning

Abstract:

Smart Predictive Systems: Integrating IoT, Data Engineering, and Machine Learning The rapid expansion of IoT devices has generated unprecedented volumes of heterogeneous data, presenting both opportunities and challenges for real-time analytics and decision-making. This topic introduces an integrated framework that synergizes IoT, data engineering, and machine learning to develop smart predictive systems. The architecture begins with efficient data ingestion using lightweight protocols and high-throughput streaming platforms, ensuring the seamless capture of sensor data. Subsequent stages involve robust data storage solutions and versatile processing pipelines that employ both batch and stream processing techniques to cleanse, transform, and integrate data across disparate sources. Central to this framework is the feature engineering phase, which prepares the data for advanced machine learning applications such as predictive maintenance and Smart Predictive detection. By leveraging edge and cloud computing resources, the system supports scalable deployment of ML models, enabling low-latency inference and continuous monitoring through feedback loops. This cohesive approach enhances operational efficiency and predictive accuracy and lays the groundwork for adaptive, real-time analytics in smart environments.