By: Shankar
May 12 2019

AIOps: Using Artificial Intelligence in DevOps

Gartner's Hype Cycle for IT Performance Analysis in 2018 states that infrastructure and operations leaders are required to make a surfeit of decisions vis-à-vis observing and assessing IT events and behaviours. This is why constructing a strategy and setting expectations while investing in technologies becomes significant. The Hype Cycle illustrated that DevOps and AIOps have gained traction among tech enthusiasts. DevOps initiatives are paramount to digital businesses but they can’t get the job done alone. DevOps is the starting point for business organisations. This will eventually pave the way for a more automated future through AIOps and result in NoOps - the point at which an IT environments turns so automated that a dedicated team is no longer required for handling tasks.

Artificial intelligence (AI) and automated operations can alter the way business operations are being managed. An automated IT approach can not only change the landscape of typical IT operations but the whole role of human involvement in IT. With orchestration and monitoring having such an integral role to play in DevOps, leveraging AI for supporting and automating operations roles is a stupendous option for delivering real-time insights about what’s happening in your IT infrastructure.

Illuminating AIOps

Horizontal 8 symbol in blue, green and yellow colours to explain AIOps and DevOps
Source: Botmetric

Gartner defines AIOps as “the application of machine learning and data science to IT operations problems”. AIOps platforms, it states, link big data machine learning functionality for improving and partially replacing all primary IT operations functions constituting availability and performance monitoring, event correlation and analysis and IT service management and automation.

AIOps is the application of machine learning and data science to IT operations problems

AIOps can transform how IT operations teams govern alerts and remediate incidents. IT can alter the DevOps pipeline via continuous alert and incident management. Leveraging data science and computational techniques, it can automate common and routine operational tasks in addition to ingesting metrics and utilising inference models for extracting actionable insights from data. Not only does AIOps offer a contextual view of service health, but also makes the tasks like monitoring, alerting and remediation simpler.

Working principle

From the process of correlation of ingested alerts to the process of triage and setting priorities to alerts for resolving them to the process of integration with IT service management tools, a typical alert management workflow involves human time to execute. On the contrary, AIOps can help in delivering intelligent alerting and automation of such tasks. AIOps perpetually learns patterns and puts them against incoming alert streams. This is done to make sense of cascading and parallel impacts. It groups related alerts into inferences on the basis of learning models.

Flowchart with rectangles and one diamond shaped box to explain AIOps and DevOps
Source: The New Stack

AIOps tools

Some DevOps tools have started to do analysis with the power of machine learning. For instance, while monitoring web apps with Azure Application Insights service, the Smart Detection feature can send an email when its machine learning powered analysis functionality detects failed requests or performance defects in page load time. Or, ScienceLogic S1 can monitor both cloud and on-premises systems. And, BMC TrueSight and OpsTamp can monitor infrastructure across numerous clouds. Using machine learning, Nastel’s AutoPilot application performance monitoring can correlate events and data from several systems.

Strategy for adoption

It is important to get acquainted with AI and machine learning vocabulary and capabilities, states Gartner, even if an AIOps project is not imminent. Moreover, initial test case should be selected wisely and illuminate AIOps for your colleagues and leadership group by demonstrating simple techniques. And once skill and experience gaps have been identified, formulate a plan on filling those gaps. There are open source and low cost machine learning software that can be leveraged for evaluating AIOps and data science applications. Also, utilise data and analytics resources that might already be available in your organisation. Moreover, your infrastructure can be prepared for a consistent automation architecture, infrastructure as code and immutable infrastructure patterns in addition to considering build vs buy continuum.

Conclusion

AIOps helps in automating the path from development to production and foretells the effect of deployment on production thereby automatically responding to alterations in the production environment. In other words, AIOps can detect issues, foretell performance hurdles, suggest optimisations, correlate signals across numerous platforms for fixing any issues, perform root cause assessment and automate fixes.