AI-powered monitoring and maintenance

We covered the critical purpose of monitoring and maintenance extensively in Chapter 16, Code Monitoring and Maintenance. We can extend that topic beyond the manual interventions and predefined benchmarks and thresholds. AI-powered monitoring and maintenance presents a shift toward a proactive and efficient approach that uses ML and other AI techniques to detect anomalies, predict bottlenecks and failures, and automate responses.

This section looks at how we can leverage AI for anomaly detection, automated monitoring, logging, and alerting. We will also explore maintenance strategies that use predictive maintenance models. Let’s get started with a look at anomaly detection using AI.

Anomaly detection

One of the primary goals of monitoring is to detect anomalies that can lead to significant issues. AI’s ability to ingest and analyze copious amounts of data empowers it to detect anomalies when the data is being reviewed by...