AI Unleashes the Prowess of DevOps Philosophy:
The scorching pace of business growth is pushing the in-place processes on the brink. The paradigm shift in the business process management shrunk the turnaround time of planning to the delivery and deployment from months to weeks. DevOps philosophy and the agile methodologies have enabled the distributed teams to communicate and collaborate in real-time across the board.
AI has transformed the DevOps streamlined software development processes by capturing data from CI/CD tools and by learning from experience to help detect a pattern to predict failures in the self-driven processes and applications.
I know that whoever is reading this right would not need me to tell what is Artificial Intelligence (AI)? Without getting mired in the nitty-gritty and AI technology crunching modalities, it is time to reinvent the potential of DevOps that integrates AI. There is no denying that in reality, AI and DevOps work hand in gloves.
AI Capabilities Redefined:
- AI helps in consuming data, learning, and getting smarter on its own.
- AI helps identify and solve problems automatically
- Independently operates without human intervention and supervision
- Drives Scalability
- Speed up business real-time
- Although simulation of human intelligence surpasses accuracy beyond human capabilities.
What is DevOps?
“DevOps is an increasingly common approach to agile software development that developers and operations teams use to build, test, deploy and monitor applications with speed, quality and control.
DevOps is relevant to any kind of software project regardless of architecture, platform or purpose. Common use cases include: cloud-native and mobile applications, application integration, and modernization and multicloud management.” -by IBM
DevOps Practice, Philosophy, and a Way of Life:
DevOps practices shorten the lengthy and time-consuming systems development life cycle. DevOps incorporate both development and the operations management facets of the software development cycle. The primary principles around DevOps are constant integration and Constant deployment.
The apparent and visibly significant benefit of the DevOps process is that its implementation runs parallel and never queued, thus making the process agile for faster evolution and improvement in the overall software development, deployment, and feedback management process. AI can speed up and multiply the impact of DevOps process implementation.
DevOps Stages and Underlying AI integration
The broad DevOps stages of software development life cycle:
- Continuous Planning
- Continuous Integration
- Continuous Testing
- Continuous Deployment
- Continuous Monitoring and Feedback
AI Speed up Continuous planning:
Continuous planning leverages AI to collect the inputs and feedback in the form of user tickets, competition analysis, surveys to form the user stories that help release the backlogs. Natural Language Processing (NLP) helps decipher the messages, emails, calls, and feedback comments on the website that provide deeper insights to the process owners and stakeholders to channelize resources and prioritize and plan for sprints and releases.
AI Fosters Continuous Integration:
AI helps continuous integration by automating the incremental builds continuously for seamless integrations of codes to minimize the risk. AI captures historical data from previous codes, builds, and logs to analyze future pitfalls. NLP can help trigger build on-demand and generate customized notifications and alerts for issues and failures.
AI Cements Continuous Testing:
Continuous testing can leverage cognitive technologies to identify life-cycle defects and speed up the time-consuming defect-tracking process. Further, AI helps create a model of analyzing logs to uncover recurring patterns for failed tests. This process prepares you to predict the causes of future test failures. NLP can aid many manual testing to convert the test cases into a script that testing tools can consume.
AI Helps Reduce Continuous Deployment Failures:
AI-enabled one-click multi-stage automated deployment management capacitated DevOps to have riddance of manually running scripts sequentially. Further, AI help parse and analyse the past deployment system and apps log to prompt the possibility of failure in subsequent deployment.
AI Automates Continuous Monitoring and Feedback:
AI captures a vast amount of data from CI/CD tools in the form of Alerts, incidents, logs, and events. Further, AI helps to produce insights from these large datasets to form ML models using supervised and unsupervised learning. These AI-based learning and ML models help identify anomalies that can cause potential vulnerabilities. Instead of having text-based surveys, AI facilitates voice-based interactive feedback capture to seek on time, and actionable inputs and provide guided help.
AI takes DevOps to a Notch Above:
Leveraging AI-based DevOps technologies will help organizations with the opportunity to enhance their data accessibility.
AI-driven DevOps solutions will help solve your pre-emptive and remove data silos by analyzing the generated data rather than working with stripped-down samples.
AI is transforming business process operations and management, particularly software development and related operational processes. AI will help you gain positive consumer experience by providing uninterrupted product availability leveraging DevOps practices of continuous integration, continuous deployment, and constant monitoring and feedback.
Integrate AI in DevOps to process a humongous amount of information by automating repetitive tasks. AI helps the routine business tasks to be automated and free up your resources to focus on improving infrastructure management to make the processes efficient and prudent.
Businesses are rapidly moving away from the traditional IT operation to embrace AIOPs to monitor the behavior by keeping the cost under check while dynamically managing the cloud resource utilization.
“AI and ML help replicate aspects of human behavior for intelligent DevOps movement, fostering the team velocity and remove human errors, thus improving the overall quality of life cycle process management.”
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