The AI and DevOps Power Duo for Optimizing Software Development

Sanam Malhotra | 21st August 2020

Analogous to two entirely different teams, Development and Operations, DevOps is the rising star of the software industry. However, the orchestration of a DevOps team demands thought leadership and advanced tools and technologies to perform efficiently. The shift in legacy software development practices is gaining momentum through the confluence of AI and DevOps.

In this edition, we at Oodles, as an Artificial Intelligence Development Company, briefly overview the role of machine learning in transforming DevOps.

 

Enterprise Challenges in DevOps Implementation

Running and managing a DevOps team is a herculean task. The complexity arises from the high magnitude of data circulating within dynamic application environments. Manual efforts by DevOps teams to absorb and channelize information mostly serve inefficient for resolving customer issues effectively.

The most pressing challenge in DevOps implementation is to adapt to new technologies for streamlining development, testing, and deployment across departments.

In addition to technological issues, below are some other challenges haunting the DevOps progress-

a) Outdated legacy systems in DevOps kill productivity

b) Data security risks with an unskilled DevOps team

c) Inconsistencies in testing processes due to miscommunication and lack of coordination, and

d) Time-consuming completion stemming from a slower error location.

IT giant, Deloitte reveals that organizations implementing DevOps observe an 18% to 21% reduction in time to market.


As automation experts, the Oodles AI team unleashes how AI can overcome these DevOps adoption challenges with ease and efficiency. 

 

How AI Streamlines DevOps and Production Cycles

While automation may not come as a first preference for combating DevOps challenges, it is the fundamental solution as businesses may find. Automated tools of collaboration, testing, and monitoring can significantly accelerate DevOps’ functioning while reducing cost and time to market.

The intersection of AI and DevOps happens when enterprises decide to embed advanced tools and technologies for software development.

DevOps can largely benefit from machine learning’s capabilities to analyze large silos of information and detect a change in patterns and performance. This ability enables developers to monitor errors while taking preemptive actions to rectify them before the complete failure of applications.

In the next section, we highlight the 3 most effective ways that enterprises can capitalize on AI to boost DevOps performance.

 

3 Effective Ways of Combining AI and DevOps

1) Testing Automation

One of the most crucial phases in DevOps, testing requires precision, attention to detail, and proficient validity. The lack of efficiency in manual software testing makes a perfect case of AI. The ability of machine learning tools to spot differences in the most complex settings enable developers to manifold testing frequency and quality.

Under DevOps, ‘Continuous Testing’ involves automatic execution of software testing resulting in better software quality and instant feedback generation. Selenium, Mocha, and Cucumber are some effective testing automation tools adaptable to different DevOps needs.

Also read | Effective AI Applications For Business Automation Journey 2020

 

2) Continuous Monitoring

Finding errors from large datasets and processes is a skill that AI is beginning to master with the right training. For DevOps, continuous monitoring is a crucial step that involves error location while tracking performance and software availability.

When integrated with machine learning, continuous monitoring can dig out errors in code, application, and development as early as possible.

In addition to error detection, machine learning can automate alert triggers for developers to take timely notice. Datadog, Prometheus, and Sensu are monitoring tools that can streamline DevOps monitoring effectively.

Also read | Techniques for Black Box Testing of Machine Learning Models

 

3) Deployment Automation

Automation in the ‘Continuous Integration/ Deployment’ phase under DevOps is essential to mitigate production risks. While merging codes in local directories, automation tools assist developers in detecting eros more quickly and easily. Also, the automatic deployment of software on the production environment eliminates the need for manual intervention.

A single manual error in the CI/CD pipeline can lead the DevOps team to painstakingly locate, communicate, fix, and recommit the code. Therefore, tools like Jenkins, Buddy, and Docker are available for simplifying software deployment through various plugins and containers.

Also read | A Complete Guide to Application Testing with AI and Machine Learning

 

Oodles AI: The Confluence of AI and DevOps

The future of software development is automated. We, at Oodles, are constantly making efforts to optimize the automation pathway for businesses. Our DevOps team is equipped with the latest tools like Jenkins, Kubernetes, Terraform, Prometheus, and more to deliver agile applications.

Connect with our DevOps team to know more about our AI capabilities and solutions.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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