The Role of Artificial Intelligence in DevOps

DevOps involves putting together people, processes, and technology to strengthen the business front. But since businesses are moving at light speed, artificial intelligence can play its part.

By overlaying artificial intelligence in DevOps, teams can automate parts of the processes. Gartner introduced it as a term ‘AIOps’ in 2016, highlighting its significance.

The central idea of using AIOps is to collect data and insights to track improvement areas. As a result, it frees the resources from guesswork and improves workflows.

Let’s discuss the ways artificial intelligence is useful for DevOps teams.

How Can DevOps Teams Leverage AI

By Collaborating Efficiently Via Natural Language Processing

Successful DevOps processes are based on communication and collaboration. AI’s Natural Language Processing (NLP) tools are beneficial in setting up virtual assistants are chatbots to facilitate just that. For example, teams can communicate with each other, automate routine tasks, and extract insights from the available data. This helps foster a culture of continuous improvement among teams.

By Optimizing Resources & Managing Costs

Since DevOps is about continuous improvement and development to reach deployment goals, idle resources or increased costs can be a killer. By utilizing AI-driven tools, teams can find cost-effective solutions while highlighting present and future resource requirements. This paves the way for efficient resource allocation and optimized infrastructure costs.

By Using AI-Driven Incident Management Tools

Recording and resolving data incidents is integral for DevOps but can be challenging when predicting future incidents. AI plays a crucial role in detecting patterns and preventing recurring issues. As a result, DevOps teams can reduce resolution times and minimize the negative impact on users.

By Automating Testing & Quality Assurance

DevOps professionals must adopt rigorous testing and quality assurance measures to ensure timely deliveries. By incorporating AI tools, teams can save the time and effort required to test vast datasets. Ultimately, it leads to identifying bugs and vulnerabilities quickly.