How DevOps Powered by AI Is Delivering Business Transformation
AI is delivering a new DevOps that identifies the system’s need to have an intelligent design, underpinned by comprehensive security.
Join the DZone community and get the full member experience.Join For Free
The application of artificial intelligence (AI) is profoundly altering our understanding of DevOps. Most importantly, it is providing the latest form of DevOps that acknowledges the need for intelligently designed applications supported by robust defence (DevSecOps).
By now, most organizations understand that DevOps is a substantial discipline they must adopt to ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.
AI-powered DevOps allows businesses to provide new value to their consumers rapidly, enabling them to mature and transform their digital images. Without DevOps, it is impossible to keep up with the competition and respond to industry events and consumer requests promptly.
This article outlines how AI business ideas that employ DevOps help better monitor, alert, and resolve issues in the production pipeline to drive strategic business benefits.
Applying AI to DevOps
AI transforms the DevOps environment in several ways such as:
Improves Data Accessibility
AI broadens the reach of data access for teams that usually face difficulty in finding publicly accessible data. AI improves teams' ability to obtain access to massive amounts of online data that are outside corporate boundaries for big data aggregation. It assists teams in having well-organized data scanned from publicly accessible databases for accurate and repeated analysis.
Offers Self Governed Systems
Before self-governing systems, software engineers typically built an application or a product feature and waited long for the IT operators to "dispatch" them. However, with the introduction of Self Governed DevOps Systems, this is not the case anymore. DevOps team can build infrastructures that allow software engineers to deploy updates without having to wait for DevOps resources to become available.
Automates App Development Process
AI's ability to automate many business processes empowers data analytics and has a huge impact on the DevOps environment. AI-enabled DevOps for app development is now a choice of many renowned firms. AI-powered DevOps allows the teams to identify solutions quickly without having to spend too much time on huge data volumes.
Intelligent Anomaly Detection
AI and ML-powered systems with their high accuracy, improve system security enabling them to deliver superior performance. With a centralized logging DevOps architecture in place, users can record and rule out any kind of suspicious activity on the network. These practices help mitigate the impact and threats posed by hackers and help companies carry out their digital transformation initiatives with precision.
Boosts Team Collaboration
DevOps powered by AI and ML allows every team to function individually without depending too much on the other. For instance, in any organization, two teams function in parallel, the developer and the operations. Now, traditionally, these two teams depend upon each other for the completion of their tasks, but with smart DevOps solutions, the two teams can function independently without having to wait for each other's approval.
Offers Better Customer Experience
Artificial Intelligence business ideas tend to have a direct impact on the productivity of an organization. With these technologies, organizations can develop and launch products faster and offer a higher level of service.
AI-Based DevOps’ Current Application
AI-based DevOps solutions are now being used by enterprises for a range of solutions, such as DevOps for app development (loan applications, mobile applications) customer churn, customer engagement, lead generation, revenue forecasting, recommendation systems, and risk scoring, etc. Sufficient computational resources on distributed computing systems are being allocated to refine model training outcomes to generate the shortest turnaround period. Compliance criteria necessitate a greater emphasis on dealing with data bias and improving model analysis capabilities.
These criteria are met by DevOps teams using CI/CD, containerized software, and microservices, which allow innovation and MLOps (Machine learning model). These procedures when employed correctly, aid in the detection and prevention of security threats, data poisoning, leaks, and system disruptions. Such measures and security are particularly important for businesses involved in sensitive infrastructure sectors such as nuclear energy, water treatment, and oil and gas. Since they use industrial control systems that have IoT sensors and protection mechanisms, they are especially vulnerable to cybersecurity attacks.
Enabling Enterprises to Adopt AI-Powered DevOps — A Way Forward
Several organizations are still in the infancy of adapting to digital transformation. This may be because of the lack of knowledge or infrastructure that they continue to work with traditional systems and have mounts of historical data in storage. AI can help fetch insights from such data and assist in the creation of applications that can enhance customer experience. It would therefore be wise for such organizations to realize these benefits and upskill their existing DevOps and data science personnel.
Data science teams in the organizations may need to stay updated on the advantages of implementing DevOps strategies such as version control for production, model lineage monitoring, model training, and testing frameworks, and so on. By defining user-specific trends in application use and tailoring features accordingly, these activities may increase gradual product delivery and personalization. Furthermore, DevOps engineers should collaborate closely with data scientists and AI- ML engineers to improve response time and monitor and control all facets of model creation and production.
The task of DevOps in digital transformation is to assist companies in understanding the trends and processes that are likely to increase their success in the face of digital change, thus enhancing their competitive position.
Transitioning from a conventional bureaucratic, command-and-control organization to a digital-ready organization where authority is delegated, individuality and alignment are matched, and everybody is encouraged to participate requires significant behavioral reform. The DevOps methodology explains why maximizing the flow from concept to meaning realization is more than just constructing a pipeline, and it also provides us with structures and models to work for from a cultural standpoint.
DevOps powered by AI will assist in making IT infrastructure more testable, resilient, measurable, dynamic, and on-demand. This aids digital transition by allowing for safer, faster improvements to the supporting IT technology, which in turn allows for safer, faster changes to software apps and services.
Opinions expressed by DZone contributors are their own.