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 write would not need me to tell you what Artificial Intelligence (AI) is.
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

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
AI helps
in consuming data, learning, and getting smarter on its own.

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
AI helps
identify and solve problems automatically

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Independently
operates without human intervention and supervision

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Drives
Scalability&nbsp;

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Speed up
business real-time

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Simulation
of human intelligence surpasses accuracy beyond human capabilities

&nbsp;

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.&nbsp;

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.&nbsp;

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:&nbsp;

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Continuous
Planning

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Continuous
Integration

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Continuous
Testing

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Continuous
Deployment

·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Continuous
Monitoring and Feedback

&nbsp;

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 analyze the past
deployment system and apps log to preempt the possibility of failure in
subsequent deployment.&nbsp;

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.&nbsp;

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.&nbsp;

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 is transforming business
process operations and management, particularly software development and
related operational processes. 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.

Artificial Intelligence Unleashes the Prowess of DevOps Philosophy

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