As digital demand escalates, AI-driven predictive maintenance is revolutionizing data center operations, paving the way for efficiency and sustainability. Data collection and preparation are vital, as high-resolution data from IoT devices and system logs form the foundation of predictive models. Advanced AI algorithms like CNNs, Random Forest, and LSTM networks enable accurate failure predictions by identifying subtle patterns in data. Avinash Pathak emphasizes that by minimizing downtime and reducing costs, AI-driven maintenance is transforming reliability and energy use, setting new standards in data center management.
Data Collection and Preparation: The Foundation of AI Insights
Effective AI-driven predictive maintenance begins with comprehensive data collection. Data centers generate vast quantities of data, from performance metrics like CPU and memory usage to environmental factors such as temperature and humidity. This high-resolution data gathered through IoT devices, system logs, and supervisory systems, forms the core of predictive models. Preprocessing this data through cleaning, normalization, and feature extraction is essential to ensure model accuracy. By refining raw …