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Modern application delivery and resilient IT infrastructure rely heavily on collaborative workflows and highly observable technical operations. Not only are developers constantly pushing code to production and changing the applications and services you maintain, but the environment around your code is changing too. So, engineering and IT organizations are adopting DevOps principles of continuous improvement, collaboration and transparency to combat unknown unknowns. On top of that, artificial intelligence is being added to DevOps and IT operations workflows – creating the term, AIOps.
AIOps is the culmination of artificial intelligence, automation, machine learning and collaboration in DevOps and IT. While AIOps specifically refers to the implementation of AI in these DevOps and operations practices, it’s influenced heavily by people and other technical processes such as automation and machine learning.
While AIOps is helping teams get better at proactively detecting problems in their applications and infrastructure, it’s still important to also have a buttoned-up process for real-time incident response. So, we wanted to cover the ways that AIOps is working with DevOps-centric teams and incident response plans to make on-call suck less and ensure service reliability.
According to Gartner, AIOps is defined as “the application of machine learning and data science to IT operations problems.” But, it’s important to make the distinction between artificial intelligence and machine learning. While machine learning can help influence AI, machine learning and artificial intelligence are not one and the same. Machine learning is the process of educating technical services via supervised and/or unsupervised learning, whereas artificial intelligence is the use of computers to execute tasks that would normally require human logic such as decision making, voice recognition or visual perception.
Artificial intelligence applied to ops technology could result in functionality like deciding to hold off on sending alerts for potentially self-correcting issues, even if it’s part of an automated escalation policy dictating that an alert should go out. Maybe the computer can detect that the system is healing, and even if the issue hadn’t fixed itself within a normal 15-minute timeframe, it will still happen soon. As the incident management system ingests more data from DevOps and IT tools, it improves the operational efficiency and automation around human workflows.
AI can help systems determine who should be fixing certain problems, when incidents should be classified as critical, and the best paths for incident resolution. Instead of forcing engineers to manually triage problems, AI can do the work for you. Alongside AI, a DevOps culture focused on transparency and collaboration can consistently lower MTTA/MTTR and facilitate a proactive incident management and response process.
In DevOps, software developers and IT professionals don’t work in isolation. Developers can’t write code and throw it over the wall to sysadmins who are in charge of deploying and maintaining uptime for services they’ve never looked at. DevOps spreads accountability for system reliability across all of engineering and IT and deepens everyone’s exposure to both staging and production environments. There’s no passing the buck when it comes to taking on-call responsibilities – the team shares service ownership and helps one another out when it comes to incident management.
When an alert comes in, who’s in charge? The DevOps team. If a service experiences downtime, who’s in charge of restoring the service? The DevOps team. This isn’t to say that every engineer on the team needs to be an expert across the entire system, but that the team has a collaborative process for working together in real-time. In a world of CI/CD, microservices and cloud-based infrastructure, developers can’t shy away from the accountability associated with service resilience in production.
DevOps isn’t a specific team or process, it’s a mindset that every engineer should adopt – from front-end developers to database admins. Implementing DevOps means you’ve dedicated yourself to becoming a customer-first business. The only surefire way to fix issues faster is with an incident management plan focused on real-time, collaborative incident response. Artificial intelligence is feeding into operations technology to feed this DevOps philosophy of collaboration and transparency.
In the 1980s, the IT Infrastructure Library (ITIL) was established as a standardized set of instructions and guidelines for how software was developed and IT infrastructure is maintained. But, the complexity of today’s software, serverless applications and cloud-based architecture means there’s no single way to organize and maintain IT infrastructure. So, DevOps and IT professionals are consistently searching for new ways to improve development speed and operational efficiency.
DevOps changed the current approach to ITIL but AI is changing it even more. Now, developers and sysadmins are able to use artificial intelligence to let computers help themselves – leading to engineering organizations focused on the future, not on the past. Developers can spend more time developing strategic future customer value instead of addressing tech debt and resolving incidents in production. AI can automatically spin up new servers based on demand or reroute incidents to an on-call engineer who’s mentioned an understanding of a particular service in chat in the past. AIOps is leading to a future of ITIL and service management where DevOps engineers and computers work in harmony.
Machines and humans generate millions of data points. Machine learning and artificial intelligence in IT operations and DevOps are constantly leading to a deeper understanding of how interconnected systems and processes work together. This can lead to more robust software and IT infrastructure in production, freeing up more time during the development lifecycle. A consistently reliable CI/CD pipeline becomes a differentiator from competitors in business and allows developers to deliver value to customers faster.
AIOps is mainly used to improve service resilience and incident response for production environments. But, a side effect of AIOps is a DevOps-centric organization that can focus more on improving the velocity of software development and delivery. IT professionals no longer create release management bottlenecks because of constant monitoring and incident response for current production incidents. Developers and sysadmins work collaboratively to share information, notify the right on-call responders and fix problems faster.
AIOps is manifesting itself in tools like Splunk ITSI or Moogsoft to help systems learn about themselves faster. Not only does machine learning help the system learn about applications and IT infrastructure faster but it can showcase important information to people faster. AIOps deepens transparency, automation and collaboration across all DevOps workflows. Combine AIOps and DevOps to facilitate the most efficient process for software delivery and incident management.
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