How To Use Artificial Intelligence to Optimize DevOps

DevOps and AI make an inseparable pair and impact businesses of all kinds. While DevOps enables speedy product development and easier maintenance of existing deployments, AI transforms the overall system functionality. The DevOps team can rely on artificial intelligence and machine learning for data integration, testing, evaluating, and releasing the system. What’s more, artificial intelligence and machine learning can improve the process driven by DevOps in an efficient, quick, and secure manner. Evaluating the importance of AI and ML in DevOps is beneficial for businesses from the standpoint of developers’ utility and business support. 

As per the Gartner report, nearly two-fifths of the DevOps community will leverage modern infrastructure and application monitoring solutions consolidated with artificial intelligence by 2023. 

So, it’s fair to say that artificial intelligence and machine learning have the power to transform the DevOps environment. For instance, artificial intelligence enables organizations to manage complex data pipelines and formulate data models to integrate with business application development. In another example, artificial intelligence helps companies map and integrate data and applications much more quickly, improving their ease of doing business, delivering delightful CXs, and accelerating revenue. 

Let’s focus on the benefits of AI and ML in transforming the DevOps environment.  

Streamline DevOps Transformation With AI 

Organizations can deliver the value promised to customers and create new revenue streams by making IT operations much more responsive and transforming DevOps processes through AI and ML. Also, they can rely on AI and ML-powered technologies to bridge the gap between people and data. ML and AI are witnessing a foray into a variety of processes as organizations focus on being data-driven. Merging AI and ML with DevOps helps organizations:

• Consider DevOps an essential aspect of digital transformation. 
• Enable efficient processes and workflows. 
• Ensure security for developing applications.

AI helps organizations identify risks by scanning a wide variety of data at speed. It reduces time and effort. To completely mitigate the chances of faults or misconfigurations, AI and ML can automate security operations. Additionally, it makes improvisations for getting rid of potential data breaches and downtime. Furthermore, the decision-making and analysis become data-driven with the support of AI and ML. 

AI Transforms DevOps in These Key Ways 

AI and ML transform the future of DevOps. It will facilitate faster voluminous computation and data in daily business operations. Following are some ways in which AI transforms DevOps:

Better Accessibility of Data

DevOps teams have to face the common challenge of navigating through unlimited data access. AI and ML take care of this through precise data transmission from the storage to the correct destination. AI prepares data gathered from myriad sources for an accurate analysis, providing valuable insights. 

Improved Effectiveness of Resources

There are plenty of repetitive operations that demand regular maintenance. AI-powered solutions can empower IT teams and non-technical business users to manage operations, ultimately increasing their effectiveness. Also, it increases the accuracy and performance of these data-driven operations. AI provides technical ease and expertise to automate repetitive and routine processes. It eliminates complexity and increases the efficiency of resources. 

Increased Data Security 

AI in DevOps ensures data security by reacting proactively to vulnerabilities. AI-powered data mapping tools help organizations map complex, bi-directional data streams much more quickly and securely. Also, these solutions offer an end-to-end encrypted environment that allows only authenticated business users to access and use data, which saves companies from breaches and thefts. AI fosters DevSecOps by monitoring threats and orchestrating ML-powered anomalies with the support of central logging infrastructure. 

Improved Decision-Making 

Organizations make better decisions with the help of recommendation engines to deliver a touch of personalization that meets customer preferences. Using AI, they can be compatible with numerous business use cases, including customer relationship management, product marketing, and lead generation. 

Better Customer Experiences 

AI-driven technologies leverage algorithms to simulate conversations. They are feasible for companies to deliver on requests. It helps them handle questions without human intervention. Also, because AI-powered data integration solutions help companies connect with customers faster, it becomes easier for them to meet the customers’ needs and requirements. Consequently, companies can ensure delightful customer experiences and improve their lifetime value. 

Conclusion 

Integrating AI with DevOps is evident. DevOps benefits from AI and ML in the form of continuous integration, continuous deployment, continuous monitoring, and continuous deployment.