Day in the life of SRE with AIOps.

Being a Site Reliability Engineer (SRE) is a complex job. To ensure everything runs well in production, you must handle code deployment, configuration, monitoring, and other related tasks. Most triage, troubleshooting, remediation, and support tasks are carried out manually. No matter how skilled you are, these procedures are laborious and prone to mistakes. The emerging tools surrounding AIOps aim to automate them.
What is AIOps?
Algorithmic IT operations, or AIOps, use data science and machine learning to resolve issues with IT operations. Artificial intelligence (AI) is used directly or indirectly by the AIOps platform to automate and enhance IT operation tasks, including monitoring, troubleshooting, resolving, and automating IT support and LI helpdesk functions.
Who is a Site Reliability Engineer?
By taking on the duties traditionally performed by operations, a site reliability engineer (SRE) builds a bridge between development and IT operations. The core responsibilities of an SRE mainly centered on standardization and automation, especially as systems move to the cloud. As a result, they frequently have experience in IT operations and a background in software, system engineering, or system administration.
Application areas in SRE for AIOps
1.Speedy Incident Resolution
SREs need to handle difficulties and situations in the right way. Complex and dynamic applications across various cloud environments are the responsibility of SRE teams. They emphasize methods for reducing hazards among end-users while preventing further accidents from happening. Large amounts of data cause numerous issues, and intelligent IT operations will assist in automating incident management, saving human labor and time. Automation can benefit from adding AI, which can speed up event response times and, in certain situations, even assist in incident prediction. 
2.Visibility Of Delivery Chain
AIOps play a crucial role in enhancing the end-user experience in the value delivery and supply chains. First, it gives a tremendous advantage in terms of experience and automation. Secondly, performance may be tracked in real-time and measured. AIOps help SREs accelerate their performance by automating manual operations and minimizing their impact on network and application performance. 
3.Noise Minimization
The primary responsibility of the SRE team is to be customer-obsessed and to ensure that the users' engagement with the application is as expected. One such service is monitoring. An SRE may be error-prone and time-consuming to manually monitor the code using conventional tools because redundant and erroneous (positive and negative) warnings may be issued. Therefore, ML tools are a major part of AIOps. Using these tools, the software can be trained continuously to identify if the alert is redundant, false, or something that needs to be dealt with immediately. This alert's identification will improve every following monitoring cycle, thus enhancing your SRE team's forecast insights. 
4.Low touch automation
With a simple button press, AIOps can provide a comprehensive and highly automated solution. Because of its AI-driven intelligence, it can handle both traditional systems and contemporary cloud-native apps. Automation can also be used in other workflows to reduce the stress on the core team and provide them more time to work on tasks that require only human intervention.
 5.Constant development
Processing operational data and simulating the end-user experience are two ways to evaluate software quality. By leveraging operational data to execute tests and confirm the app's health, businesses may continuously improve the quality of their apps. Any incidents can be resolved to help make the application stable and user-friendly. Given that it will never produce results as precise as operational data, it is preferable to use mock data. As a result, the SDLC is greatly enhanced, and results can be measured with greater accuracy.
The role of AIOps in SRE's lives
1.Automatic diagnosis and constant development
Since AIOps operates continuously, real-time error reporting is possible.
2.Time-saving
When time is of the essence, AIOps are excellent.ML helps with quick data processing, which humans need help with. 
3.Increased  level of service experience
When less time is spent on finding and addressing problems, the user experience is naturally improved. 
4.Efficiency in operations
Machine learning increases operational effectiveness. Errors are found much more quickly, and corrections can be made to lessen them. 
5.Reduced manual errors
Since ML does not experience human weariness, it can operate continuously. There is no space for error at all. 
Conclusion
AIOps is not used to replace SREs', which contradicts the popular belief. AIOps is a supplement to SREs'.SREs' will continue to serve this purpose, and AIOps will hasten the software development process while lowering incidents. SREs' jobs will be easier because of automation, which also offers a backup plan for any eventuality. Efficiency will automatically increase, and the failure rate will decrease. Eventually, the use of AIOps in SRE will grow in the coming years. To know more about Algomox AIOps,please visit our Algomox AIOps Platform Page.