The AI Ops workflow involves several steps to implement and operate AI-based automation in IT operations. Here is a high-level overview of the typical AiOps workflow:
- Data Collection: The first step is to collect data from various sources such as logs, metrics, and events generated by applications, infrastructure, and network devices. This data is then processed and normalized to be ready for analysis.
- Data Analysis: The next step is to apply analytics and machine learning algorithms to the collected data. This involves identifying patterns, anomalies, and trends that can help detect and diagnose issues faster and more accurately. AI algorithms can also predict future incidents and identify the root cause of the problem.
- Event Correlation: After analyzing the data, the system correlates events across different sources to create a meaningful context for the alerts generated by the system. This helps IT teams understand the impact of the issue on the overall system and prioritize their response accordingly.
- Alert Prioritization: Based on the severity and impact of the incidents detected, the system generates alerts and assigns a priority level to each of them. This helps IT teams prioritize their response and focus on the most critical issues first.
- Incident Management: Once an alert is generated, the system initiates an incident management process to address the issue. The system can also automate the resolution process for known issues, or provide recommendations to IT teams to resolve the issue more efficiently.
- Continuous Improvement: As the system continues to analyze and process data, it learns from the outcomes of the incident management process. This helps the system continuously improve and refine its analysis and recommendations, resulting in better incident management over time.