Part 4/11:
Data Collection: Gathering logs, metrics, traces from servers, databases, and applications.
Data Analysis: Applying machine learning algorithms to identify anomalies, patterns, and potential issues.
Acting on Insights: Prioritizing alerts, performing root cause analysis, and automating responses to resolve issues proactively.
By orchestrating these steps, AI Ops aims to reduce noise, eliminate repetitive manual tasks, and enable faster problem resolution.
How AI Ops Works: The Process in Detail
The process begins with data injection—collecting data from diverse sources like cloud services, applications, and network devices. This raw data is then stored in large-scale storage systems, whether in structured databases or unstructured repositories.