Cloud, Agile, and DevOps have revolutionized how software is developed and consumed. Speed, dynamic implementation of changes, global presence, and quality are the areas that the IT revolution has influenced. While organizations are banking on the newer tech stacks and platforms to cater to the demands of a pool of ever-increasing users, it has become imperative to balance agility with reliability.

Site Reliability Engineering (SRE) is an approach that deals with the operations, scalability, and reliability aspects of the development process. SRE applies the elements of software engineering to operations and infrastructure problems. The primary goal is to create scalable and highly reliable systems by offering solutions to handle complex operations. We can consider SRE as a specific implementation of DevOps. While DevOps is more focused on Ops and development pipelines, SRE focuses on ensuring the operations run as expected. Benjamin Treynor Sloss developed it in 2003 and since then, has been an integral part of the DevOps process in many organizations. Simply put, SRE comes into play when preparing for failures in production. It helps companies boost the reliability of their site infrastructure by spotting failures, identifying backup plans, and taking steps to mitigate risks that may occur from failures in the future.

It is often said that data is the next biggest commodity. Thanks to the advancements in technology, we have plenty of it. The data that is produced and consumed right now is vast. Managing vast amounts of data is difficult, but getting actionable outputs is humanly impossible. Effective management of enormous amounts of data needs systems in place, and that is where Artificial Intelligence for IT Operations (AIOps) comes in. AIOps platforms lean on machine learning, big data, and other data engineering techniques for monitoring and automation to enhance IT operations. They use different data collection methods to gather data, process it, and derive outputs that are valuable and can be put to use. Simply put, you are creating a process where you can get actionable items from your data.

How AIOps can solve SRE problems

SRE tracks and resolves IT outages before end-users are affected. However, monitoring all the processes and data in real-time can be challenging. This is where AIOps can be handy for the SRE teams. AIOps can provide reliable support with specialized proactive monitoring, warning, and reporting systems that will inform about the issues and incidents before they get out of hand and affect users. This saves considerable time and effort for SREs and directly benefits end-users. AI involves less manual work and needs lesser technical staff and fewer engineers to monitor or highlight problems in advance.

Let's see how AIOps can augment your SRE initiatives.

Application of AIOps by SRE teams

Here are some of the most common application areas in SRE for AIOps:

• Faster incident resolution: SREs must appropriately respond to challenges and manage incidents. SRE teams are responsible for complex and dynamic applications across different cloud environments. They focus on techniques that help avoid past incidents while mitigating risks at the end-user level. Vast amounts of data bring in multiple challenges, and Intelligent IT operations help them automate incident management, saving a lot of manual effort and time. AI can add intelligence to your automation and provide faster incident resolutions, and in some cases, can help predict the incidences before they happen.
• Minimizing the noise: Noise minimization means bringing down incidents and time to respond to incidents. Monitoring techniques of the past are not efficient enough to track the ever-increasing number of app processes, users, and incidents. To improve user experience and engagement, organizations need to improve reliability. With AI and ML, you can detect and set a priority on incidents with predefined actions to be taken. With AIOps and automated course correction actions, the core teams will have more time to focus on more significant issues.
• Intelligent operations: Intelligent systems are created to relieve manual efforts. Once manual steps are reduced, teams can focus on innovation, application enhancement, and development. AIOps, with the help of ML, can detect incident patterns and flag them before they turn into more significant issues. AIOps can collect and synthesize large amounts of data and run real-time analyses to identify patterns. It helps teams with rapid response, ensuring minimal time pinpointing the affected areas and stabilizing the program. This helps them quickly solve complex issues and fulfill service-level and user-experience goals.
• Delivery chain visibility: Visibility can also mean transparency. Because of supply chain visibility, teams can see where they are about their goals. Two essential aspects of the organization can be satisfied here. Firstly, the user experience is improved. AIOps play a vital role in enhancing the end-user experience through predictive analysis. This is a massive benefit. Secondly, performance can be measured and monitored in real time. AIOps help improves the performance of the network and applications by minimizing/eliminating manual tasks by automation, boosting the overall performance of SREs.
• Zero-touch automation: AIOps play a considerable role in delivering a comprehensive and fully orchestrated service with just a click of a button. It is powerful enough to handle modern cloud-native applications and traditional mainframes. Automation can also be applied to other workflows to ease the workload of the core team and provide them with bandwidth to work on tasks that need absolute human intervention.
• Continuous improvement: Software quality is measured by processing operational data and replicating the end-user experience. Organizations can continuously improve app quality by using operational data to run tests and verify the app's health. Any incidents that occur can be fixed to help make the application stable and user-friendly. It is better than using mock data since it will never return the exact results of operational data. Thus, the SDLC is immensely improved, and more accurate results can be measured.

The role of AIOps in SRE’s lives

AIOps helps SREs a lot, but here are the top five:

• Automatic diagnosis and continuous improvement: Real-time error reporting is possible since AIOps functions round the clock.
• Time-saving: AIOps play a perfect role when time is of the essence. ML assists in fast data processing, something that humans can't keep up with.
• Increased service levels: User experience is automatically enhanced when less time is spent identifying and fixing issues.
• Efficient operations: Operational efficiency gets a boost due to machine learning. Faults are identified much sooner, and rectifications can be made to mitigate these faults.
• Reduced human errors: ML does not face issues like human fatigue and can work round the clock – non-stop. It leaves absolutely no room for error.

The future of SRE with AIOps

People might think AIOps are used to eliminate or replace SREs, which is not the case. In reality, AIOps complement SREs. The function of SREs will always be around, and AIOps will only speed up the SDLC process while reducing incidences. Automation will make the job of SREs easier and provide a contingency plan for every situation. The rate of failures will be diminished, and efficiency will automatically get a boost. Experts predict that the use of AIOps in SRE will grow by 37% by 2023, an optimistic industry figure.