Advances In Economical Maintenance Practices For Ai And Ml Deployments In The Industry Of Information Technology

Abstract

Integrating AI and ML into IT infrastructures has improved data analytics and increased operational efficiency, which has revolutionized corporate operations. Nevertheless, there are some clear obstacles that need to be overcome for these ML and AI systems to continue functioning consistently and efficiently, especially in terms of cost control. This study aims to identify the most cost-effective methods for enhancing maintenance solutions for AI and ML installations. Ongoing monitoring, frequent model updates, and retraining are necessary for AI and ML systems to retain accuracy and adapt to changing data patterns. This is because of the inherent complexity of these systems. When these systems are uncooperative with routine maintenance, expenses may easily get out of hand and performance suffers. Predictive maintenance is one of the innovative methods highlighted in the article; it uses ML algorithms to foresee potential issues and save repair costs and downtime by doing preventative maintenance. Also covered is the possibility that automated monitoring systems might quickly spot outliers, therefore reducing the need for human supervision. The researchers want to learn, among other things, what the optimal mix of upfront investment and ongoing operating expenses for maintenance is for reliable infrastructure. Organizations may potentially make better use of their res the ces, decrease risks, and increase the life of their AI and ML systems by being proactive and taking steps to prevent difficulties. The results give a road map for IT specialists and decision-makers to follow when creating cost-effective maintenance strategies, which might be very helpful. By drawing attention to the need of strategic maintenance in order to maximize the return on investment (ROI) in these revolutionary technologies, the researcher's work adds to the continuing conversation on sustainable AI and ML management.