AIOps: The Future of DevOps
Since entering the scene in the late 2000s, DevOps has changed the way development and IT operations teams collaborate together to produce and deploy new digital services, apps, features, and updates. DevOps has helped to streamline the production process to ensure more efficient workflows and a more reliable service. This created a shared responsibility between development and operations teams to collaborate together with the goal of creating a premium product or service for their customers. However, there was still a constant challenge for many teams as services were constantly changing, or having new features and updates rolled out. IT teams had to quickly detect and resolve incidents while avoiding unplanned downtime.
Enter AIOps. With the implementation of artificial intelligence and machine learning, AIOps—which stands for Artificial Intelligence for IT Operations—allowed IT teams to automate incident detection and remediation. This ultimately helped free up time from handling outage emergencies and allowed IT teams to focus on the bigger picture: the customer experience.
In this article, we’ll look at how we got to AIOps, and what it means for the future of DevOps teams like your own.
A Look Back: What Led Us Here?
DevOps began as a way to create a more collaborative work culture between developers and IT operations. In a traditional production team setting, developers worked separately from operations, and it was fairly typical for developers to simply hand over their code to a central IT team and forget about it. With DevOps, there is a shared ownership of the product, and teams work together without siloes to ensure a more streamlined and efficient workflow. With DevOps, production teams were able to deploy new services and updates more quickly, and developers were able to focus on creating new and innovative features without handling constant escalations.
However, while DevOps changed the game for production and deployment processes, there was another challenge that teams were still facing. Whenever an incident would occur, it was up to the SREs (Site Reliability Engineers) and DevOps teams to detect and resolve the issue. This meant having to sift through all the different alert noise to identify where incidents are occurring within the service or infrastructure. Teams would also need to understand the different relationships between specific data points and determine which teams or people need to be alerted to resolve an issue.
In order to maintain an optimal user experience and avoid downtimes, IT teams were often focused on fixing customer-impacting outages and emergencies. Because of this, workers became less-agile and flexible, having to focus on resolving incidents as quickly as possible. Time for innovation was slim to none.
What teams needed was a way to monitor all the different data points within their applications and infrastructures while quickly detecting and resolving incidents as they occur in real time. We have already seen many of the incredible benefits that artificial intelligence (AI) and machine learning (ML) can have in the real world, using complex algorithms to detect specific patterns in data and learning from the data overtime. For example, think of how Google sometimes finishes your thought when you begin entering terms into a search bar. Or how your directions app can change and adjust your route based on traffic patterns and real-time updates.
So, when it comes to DevOps, AI technology and tools helped take automation and efficiency a step further. AIOps helped tackle the challenge IT operations teams faced with incident detection and resolution, automating those tasks to help detect and fix incidents in real time – even preventing them from occurring.
What AIOps Can Do for DevOps Teams
As you see, AIOps and DevOps work together to help development, production, and operations teams collaborate more effectively, work more efficiently, all while focusing on the customer. AIOps can help benefit DevOps teams in several key ways, including:
Allow operators to work smarter. With AIOps, there is a greater focus on improving and building a scalable and reliable service rather than simply keeping it functional.
Smart noise reduction. By adding AI throughout your infrastructure, workflows are streamlined as machine learning algorithms adapt to your team’s specific needs and environment, grouping alerts and helping to sift through the noise to actionable alerts.
Learn from past & related incidents. As incidents are detected and resolved, AIOps learns from these occurrences and can detect patterns based on previous incidents. By learning from these incidents over time, AIOps can detect anomalies that deviate from known patterns and predict incidents before they occur.
Automate routine remediation tasks. AIOps makes automatic remediation possible by learning from and adapting to incidents as they are detected and resolved. This means that AIOps can trigger custom actions to run remediation, often even preventing it from happening.
Who is AIOps For?
AIOps is great for DevOps teams wanting to take advantage of the many benefits offered by artificial intelligence and machine learning. With proactive incident detective and automated remediation, IT operations teams are free to focus on improving services to provide customers with an optimal user experience. AIOps helps ensure service reliability without having to constantly scale up operations teams strictly to keep the product functioning. The emphasis is now on innovation and creativity – all while artificial intelligence and machine learning help to improve incident response and minimize unplanned downtime.
How to Integrate AIOps for DevOps
AIOps easily integrates with many existing tools and processes, helping teams get the most out of their many data streams generated by different applications and infrastructures. What AIOps does is digest all these different data points and analyzes them in order to understand the different relationships within the data and effectively monitor the system to ensure proper functionality at all times.
Finally, one of the most important benefits of leveraging AIOps and automation in your workflows is preventing burnout. Without worrying about constantly resolving emergencies, teams can now focus on what they do best: being creative and innovative. When their only focus is on fixing issues, operators are more likely to burn out. With AIOps able to automate these tasks, teams are free to focus more on improving a product or service and creating the best possible customer experience.