AI-Augmented Release Management for Enterprises in Manufacturing: Leveraging Machine Learning to Optimize Software Deployment Cycles and Minimize Production Disruptions
Keywords:
AI-augmented release management, machine learning, software deployment cyclesAbstract
CI/CD pipelines of industrial software development have greatly improved production efficiency and operational efficacy. Complicated release management systems of big companies might cause system failures, deployment issues, and production interruptions, therefore affecting manufacturing. The present work explores how artificial intelligence—particularly machine learning—may improve industrial release control. This work suggests that ML models might assist with manufacturing interruptions, release choices, and software deployment.
Dependency problems, environmental inconsistencies, manufacturing release management, and unexpected runtime flaws are found from manufacturing to deployment. Machine learning approaches enable pre-deployment testing, real-time risk assessment, and rollback to improve release management decision-making automated. Using ML models to spot trends and project problems in early deployment data helps companies avoid developing concerns. Predictive skills are essential in manufacturing as even minor software implementation problems might be costly and cause disturbance of operations.
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