OPTIMIZING COST-EFFECTIVE MAINTENANCE STRATEGIES FOR AI AND MACHINE LEARNING IMPLEMENTATIONS IN INFORMATION TECHNOLOGY

Abstract

Organisational operations have been drastically altered by the integration of AI and ML into IT infrastructures, which has allowed for enhanced data analytics and increased efficiency. Nevertheless, there are unique obstacles to be overcome when it comes to maintaining these AI and ML systems, especially when it comes to controlling expenses while guaranteeing continued performance and dependability. The need to optimise maintenance solutions that are cost-effective and particularly designed for AI and ML installations is addressed in this study. Because of their intrinsic complexity, AI and ML systems require constant vigilance, frequent model changes, and retraining at regular intervals to keep up with ever-changing data trends and preserve accuracy. These systems often defy conventional maintenance methods, which may cause expenses to skyrocket and performance to suffer. Predictive maintenance, which uses ML algorithms to foresee faults before they happen, reduces repair costs and downtime, is one of the unique solutions explored in the article. Also covered is the possibility of using automated monitoring technologies to cut down on labor-intensive human supervision by quickly spotting outliers. The optimal mix of up-front investment in reliable infrastructure and ongoing operating expenses for upkeep is a primary concern of this study. Organisations may improve the total lifetime of their AI and ML systems, optimise resource allocation, and limit risks by taking a proactive and preventative strategy. Information technology (IT) experts and decision-makers may benefit greatly from the provided results, which lay out a framework for creating successful and cost-efficient maintenance plans. To maximise the return on investment (ROI) in these revolutionary technologies, our study adds to the continuing conversation on sustainable AI and ML management by emphasising the need of strategic maintenance.