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 just trying to identify the issue.

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 critical tool for computing and analyzing and transforming how teams develop, deliver, deploy, and manage applications. But before we explore how AI/ML will transform DevOps, let’s first understand how AI and DevOps are interrelated.

How DevOps and AI operate together

DevOps and AI are interdependent as DevOps is a business-driven approach to deliver software, and AI is the technology that can be integrated into the system for enhanced functionality. With the help of AI, DevOps teams can test, code, release, and monitor software more efficiently. AI can also improve automation, quickly identify and resolve issues, and improve collaboration between teams.

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AI can play a crucial role in accelerating DevOps efficiency. It can boost performance by enabling instant development and operation cycles and delivering a compelling customer experience on these features. Machine learning systems can simplify data collection from various parts of the DevOps system. This includes velocity, defects found, and burn rate, which is more traditional development metrics. Data generated by continuous integration and deployment of tools is also a part of DevOps. Metrics like the number of integrations, the time between them, its success rate, and defects per integration are only valuable when they are accurately evaluated and correlated. Here are 12 ways artificial intelligence is transforming DevOps:

1. Software testing

AI is an asset to DevOps, as it enhances the software development process and makes testing more efficient. A large amount of data is produced through regression testing, functional testing, or user acceptance testing. And AI can decipher the pattern in the data collected by producing the outcome and help identify mediocre coding practices that are responsible for numerous errors. Such information can be used to increase efficiency.

2. 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.

3. Timely alerts

DevOps teams need to have a well-developed alert system to spot flaws instantly. At times alerts come in huge numbers, and all are marked with the same severity. This makes it very difficult for teams to react and respond. AI and ML can help teams prioritize their responses based on certain factors like past behavior, the intensity of the alert, and the source of the alerts. They can efficiently manage such situations when systems are flooded with data.

4. 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 agents can achieve, but also to enable a level of change adaptation that hasn’t been possible.

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 help in predicting an error based on 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).

6. 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.

7. Faster root cause analysis

AI utilizes the patterns between cause and activity to determine the root cause behind the failure. 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 makes things work, the root cause remains unknown. It is, therefore, imperative to fix a problem permanently by conducting root cause analysis. Artificial Intelligence plays a vital role here.

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8. Feedback loop

The primary function of DevOps is to gather feedback at every stage. Performance monitoring tools are often used to collect feedback. These monitoring tools use machine learning (ML) to gather information such as log files, performance matrix, datasheets, and more, which are used to identify issues in advance and make the suggestions accordingly. These suggestions are then applied to make alterations in the applications.

9. Anomaly detection

Since security is essential to any successful software implementation, DevSecOps is one of the most important aspects of software development. Businesses must protect their security systems as there has been an increase in Distribution Denial of Services (DDoS) attacks, and there is a constant threat of hackers breaching the secure system. AI can be used to augment DevSecOps and enhance security by recording threats and running ML-based anomaly detection by a central logging architecture. A proactive strategy by combining AI and DevOps will ensure maximum performance and will prevent attacks from DDoS and hackers.

10. More efficient collaboration

As developers are required to release code at high velocity, the operations teams have to ensure minimum disruption to the existing systems. AI can transform DevOps by improving the collaboration between development and operations teams. The AI-powered systems can aid the teams by providing a single, unified view into systems and their issues across the complex chain of DevOps. Simultaneously, it can improve the complete understanding and knowledge of anomalies detected and rectify it instantly.

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11. Instant redressal of issues

Software bugs and issues are significant hindrances to operational efficiency. AI can transform DevOps by finding the problem and rectifying it immediately. AI also helps in prioritizing the most severe issues hampering the performance of the application, collecting relevant diagnostic data regarding the issue, and also recommending solutions. After discovering the problem, ML can also help in analyzing the impact of the solution by training data sets. AI systems can be even more accurate by offering recommendations and providing instant solutions.

12. Analyzing past performances

Machine learning can be a great asset to developers during the application creation process. It can help examine the success of previous applications in terms of compile/build success, operation performance, and successful testing completion. ML can also proactively provide recommendations depending on the code being written by the developer. AI can guide the developer to build the most efficient, distinct, and premier application.

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