Vamsi Krishna Reddy Munnangi at TechBullion examines AI-driven machine learning models that analyze API traffic, predict demand, and implement adaptive caching. The approach enhances performance by reducing latency, fortifies security through anomaly detection, and introduces predictive maintenance to anticipate failures, ensuring resilient, self-healing cloud-native API infrastructures for modern distributed systems.

Key points

• Machine learning algorithms analyze API traffic patterns and dynamically allocate resources, cutting response latency by up to 25%.
• AI-driven anomaly detection monitors millions of API events per second, identifying security threats and reducing incident detection time by over 50%.
• Predictive maintenance models forecast API failures and enable self-healing by auto-restarting services and rerouting traffic, reducing unplanned downtime by up to 70%.

Why it matters: By automating performance optimization, security monitoring, and maintenance, this AI-driven model transforms API operations with unprecedented efficiency and resilience.