Developing Inexpensive Maintenance Plans For Machine Learning And Ai Applications In Technological Systems

Abstract

By enhancing data analytics and increasing operational efficiency via their integration into IT infrastructures, artificial intelligence (AI) and machine learning (ML) have transformed corporate operations. To maintain the efficient and dependable operation of these AI and ML systems, however, some clear obstacles must be overcome, most notably in the area of cost control. Discovering the most cost-effective methods to enhance maintenance solutions for AI and ML installations is the primary goal of this study. To maintain accuracy and keep up with constantly changing data patterns, AI and ML systems need constant monitoring, frequent model changes, and retraining. This is because these systems are inherently complicated. Expenses may quickly get out of hand and performance plummets when these systems resist conventional maintenance practices. Proactive maintenance, which uses ML algorithms to foresee issues before they happen and save repair costs and downtime, is one of the innovative ideas mentioned in the article. The researchers also talk about how automated monitoring systems may spot abnormalities quickly, which might cut down on human supervision. Finding the optimal mix of upfront investment in reliable infrastructure and ongoing operating expenses for maintenance is one of the primary objectives of this study. Businesses may better use their resources, decrease risks, and increase the lifespan of their AI and ML systems if they take preventative actions. Potentially helpful for decision-makers and IT professionals, the results lay out a framework for creating cost-effective maintenance plans. This study adds to the current conversation on long-term AI and ML management practices by demonstrating how important strategic maintenance is for getting the most out of these innovative tools.