5 Ways Artificial Intelligence is Transforming DevOps
Feb 19, 2018
Monitoring and managing a DevOps environment involves a high degree of complexity. The sheer magnitude of data in today’s dynamic and distributed application environments has made it difficult for DevOps teams to effectively absorb and apply information to address and resolve customer issues.
Imagine a team navigating through Exabytes of information to find critical events that triggered an event − they would end up spending hundreds of hours in just trying to identify the issue. The amount of work involved is and the extent of resource.
The future of DevOps will be AI driven. Since humans are not equipped to handle the massive volumes of data and computing in daily operations, Artificial Intelligence will become the key tool for computing, analyzing and decision-making processes.
Artificial Intelligence is set to transform how teams develop, deliver, deploy, and manage applications. Here are 5 ways Artificial Intelligence will impact DevOps:
1. Improved data access
The lack of unfettered access to data is among the most critical issues faced by DevOps teams. Artificial Intelligence will help liberate data from its organizational silos for big data aggregation. AI can collate data from multiple sources, and organize it to be useful for consistent and repeatable analysis.
2. Superior execution efficiency
Artificial Intelligence is driving the transition from a rule-based, human management of analysis to self-governed systems. This is required not only because of limits to the complexity of analysis human agents can achieve, but also to enable a level of change adaptation that hasn’t been possible.
3. Smarter resource management
Artificial Intelligence provides the much needed capability to automate routine, repeatable tasks. As AI and machine learning evolve, the scope and complexity of the tasks that can be automated increases, and humans will be able to focus on more innovation and creativity.
4. Faster root cause analysis
AI utilizes the patterns between cause and activity to determine the root cause behind the failure, by considering all the data. Often, engineers don’t investigate failures in detail as they are mostly focused on Going Live. They analyze and resolve issues superficially and avoid detailed root cause analysis. If superficially resolving the issue make things work, the root cause remains unknown. It is therefore imperative to fix an issue permanently by conducting root cause analysis. Artificial Intelligence plays a key role here.
5. Swifter failure forecasting
A major failure in a particular area/tool in DevOps can weaken the process and slow down the cycles. Machine learning models helps predicting an error based on enough data. AI has the ability to read patterns and predict the signs of failure, especially when an occurred fault is known to produce definite readings. AI is capable of seeing indicators which humans cannot perceive. Such early predictions and notifications help the team to identify and fix the issues before they have an impact on the software development life cycle (SDLC).
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About TA Digital
TA Digital is an innovative digital transformation agency, specializing in delivering digital experience, commerce, and marketing solutions. For nearly two decades, we have been helping traditional businesses transform and create dynamic digital cultures through disruptive strategies and agile deployment of innovative solutions. We are known as a global leader in the digital technology industry for helping marketing leaders achieve their revenue targets, create profitable, omni-channel customer and commerce experiences. TA Digital has high-level strategic partnerships with digital technology companies Adobe, Microsoft, Sitecore, Acquia, Marketo, SAP Hybris, Elastic Path, IBM Watson Marketing, Coveo and Episerver. The company was named on 2013, 2014, 2015 Inc. 5000 list as one of the fastest-growing technology companies in the United States.