AIOps’ goal is to automate the tasks of Site Reliability Engineers. SREs have to do many complicated tasks like code deployment, configuration, monitoring, etc. They ensure the smoothness of the works in production. They also need to manage triage, troubleshooting, remediation, and continuous support. Manually doing these tasks takes a lot of effort. That is why AIOps is here to automate as many tasks as possible.
AIOps in SREs Daily Work
AIOps is the combination of Artificial Intelligence and IT operations. It uses ML and automated techniques to analyze Big Data, detect bugs, and speed up the process. It uses some features to help SREs. Let’s take a look at them:
Correlate and Analyze Datasets
In the SRE working method, engineers need to consume and correlate datasets from multiple architectural layers. AIOps uses Topology Analytics to find the root cause of issues and remove them. This process is faster than manually fixing the bugs. It visualizes essential parts of the delivery chain like user experience and network traffic. Network performance improves as manual tasks reduce. It gets easy to manage traffic. Users’ experience also increases by continuous delivery of quality software.
Zero-touch automation is always at SRE’s service. AIOps is ready to automate as many tasks as possible. It automates the entire stack, including mainframes and cloud-native applications. This application can be both microservices and serverless. Automation enhances the configuration process.
Future Prediction and Alarms
SREs core work is to enhance customers’ experience and engage them more with the application. They need to monitor the service continuously. Traditionally, SREs do the monitoring tasks manually. Though it takes a lot of time, it cannot give error-free results. Sometimes traditional tools send false alarms, which create unnecessary noises. AIOps uses ML to continuously train the tools to identify the false alarm and immediately remove them. It also improves the predictive insights.
AIOps continuously collects adequate amounts of data from Dev and Ops teams. It uses these resources to improve SDLC. It avoids mock data to ensure the software can function in a diverse environment. Operators’ efficiency increases as manual tasks are eliminated. Visualization of the process improves the scalability of the software.
Automation in operational tasks ensures error-free diagnostics and overall improvement through the entire SDLC. Reducing alarms helps operators to work in a sound environment. To get the best output from the operation team, businesses are considering AIOps.