Artificial intelligence (AI) plays a key role in application monitoring, also known as Application Performance Monitoring (APM). 

Modern IT networks have become increasingly complex and distributed

To the point where simple network monitoring management techniques are insufficient. 

The distributed systems of modern IT networks generate a large amount of data that must be monitored and analyzed to ensure a smooth workflow. 

Enterprises have moved to the practice of observing their environment to ensure operation and performance, an effort that is aided by full observability.

Full observability includes the practice of collecting, analyzing and acting on data generated by various components within an IT ecosystem. 

It requires a holistic approach. Rather than focusing on individual components, it encompasses every layer of an application, from infrastructure and network to application code and end-user interactions.

Full observability combines the advantages of AI and ML algorithms to forecast outages, detect deficiencies and diagnose any complex problem in a network environment.

The following are some of the ways in which AI is used in this context:

Anomaly detection: AI is used to automatically detect anomalies in application performance. Machine learning algorithms can analyze real-time and historical data to identify anomalous patterns that could indicate performance problems, such as drops in response speed or unusual errors.

Problem prediction: AI can predict potential problems in an application before they occur. This is achieved by identifying trends and patterns that could lead to future problems, allowing operations and development teams to take preventative action.

Performance optimization: AI-powered application monitoring systems can provide recommendations for optimizing an application’s performance. This could include suggestions on server configuration, resource management or bottleneck resolution.

Automation of corrective actions: In cases of performance problems, AI can be used to automate corrective actions. For example, if an increase in CPU usage is detected, AI can automatically adjust resource allocation or restart a server.

Root cause analysis: AI can help identify the root cause of performance problems by analyzing multiple data sources and correlating events. This enables faster and more efficient problem resolution.

Reporting and data visualization: AI-powered APM systems can generate detailed reports and data visualizations to help teams better understand application performance. This makes it easier to make informed decisions and communicate issues to all stakeholders.

Capacity management: AI can help predict an application’s future capacity needs based on historical and current trends, enabling more effective planning.

Security: AI is also used in application monitoring to detect potential security threats, such as intrusions or suspicious activity. This is especially important in applications that handle sensitive data.

Complete visibility into your infrastructure and applications

In a nutshell,

AI is playing an increasingly important role in application monitoring by improving problem detection capabilities, decision making and efficiency in application performance management. This is essential to ensure the availability, performance and security of applications in enterprise and online environments.

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