AI and DevOps together make a powerful combination of efficiency and intelligence

Supervising and managing a DevOps environment can be complex. The proliferation of data has made it challenging for DevOps teams to effectively absorb and implement information to evaluate and tackle customer issues. Imagine a team navigating through data in exabytes to search for important events that triggered an event; they would end up investing hundreds of hours in identifying the issue.

A lot of such critical issues can be resolved with artificial intelligence (AI)-powered technologies. Organizations can transform their DevOps environment by deploying AI systems. Since it is difficult for humans to handle large volumes of data, AI has gained traction as the solution that transforms the way teams develop, deploy, deliver and manage applications. Before understanding the role of AI in transforming DevOps, however, we first must understand how AI and DevOps are interrelated.

DevOps and AI Work Together

DevOps takes a business-driven approach to deliver, and AI can be integrated into the system for improved functionality. By using AI-powered technologies, DevOps teams can utilize volumes of information to the best of their ability, which in turn helps streamline testing, coding, releasing and monitoring products with precision and efficiency. It also plays a central role in improving automation, quickly identifying and resolving issues and enhancing collaboration amidst teams.

AI provides a major boost to DevOps efficiency. Various AI tools enable users to map and integrate data at the speed of business, driving business decision-making and delivering delightful customer experiences. Machine learning systems can simplify the way DevOps systems work with data to deliver value.

Here are five reasons why implementing artificial intelligence-based solutions in a DevOps culture is necessary:

1. Increased Data Access

The lack of unfettered data access is one of the most important issues that surfaced in the DevOps environment. It inhibits business users’ capability to leverage data for making decisions, ultimately making organizations difficult to do business with. AI-powered data mapping technologies enable them to deal with data silos that can help them get the right information at the right time so they can make good decisions. AI-powered data mapping, for example, helps users map troves of fragmented data faster, which can then be integrated into a unified database. They can employ machine learning algorithms to map data faster without succumbing to inaccuracies or errors.

In short, machine learning data mapping tools empower users to map big data with ease and precision. Moreover, they can integrate the data silos present across the business ecosystems to gain accurate customer insights to improve data quality and efficiency. So, AI can tackle organizational silos to help users make decisions without compromising speed or accuracy. AI can combine data from multiple sources and get it ready for consistent and repeatable analysis.

2. Timely Notifications and Alerts 

DevOps teams must have good alerts and notifications systems to identify flaws without delay. There are times when notifications come in enormous numbers, all marked with the same severity. Such huge numbers can have a negative impact on the response rate of teams. AI can help prioritize the responses based on the information derived from past behavior, the intensity of the alert and the source of the alerts. As a result, teams can manage the tsunami of data with confidence.

3. Improved Execution Efficiency 

The transition from a rule-based, human management of analysis to self-governed systems can be realized through AI. This helps business users alleviate the complexity of analysis and enable a level of change adaptation for digital transformation.

4. Anomaly Detection Capabilities

For any business, protecting security systems is extremely important. AI and machine learning can help enhance security by enabling an encrypted data environment wherein, only authorized personnel have the authentication to access data (AI-powered data integration tools play a vital role). Machine learning algorithms help users streamline anomaly detection to boot. Simply put, combining AI and DevOps will facilitate performance and stop breaches or thefts.

5. Efficient Collaboration

The minimum the disruption is, the maximum is productivity and collaboration. And AI mechanisms help developers release code at high velocity to minimize the disruption. AI can create a collaborative DevOps environment that helps companies deliver maximum value, maximizing ROI and ultimately making them easier to do business with.

AI-powered systems can support the teams by offering a single, unified view into data and applications across the chain of DevOps. Along with that, it can improve the accuracy of data analysis as anomalies can be detected and rectified right away.