Harnessing the Power of AI in DevOps - A Comprehensive Guide

Artificial intelligence is revolutionizing DevOps. 
And you don’t have to take our word for it! 

Stats show that over 90% of developers use AI tools to write at least part of their code. 

Google CEO Sundar Pichai recently said that over 25% of its new code is written by AI. 

So, what does this mean for the world of DevOps? 

What benefits can you get from deploying AI and DevOps together?

How can you harness the power of AI in your software development operations? 

In this blog, we will discuss the emerging role of AI in DevOps, along with all its implications for software development.

We’ll also tell you how you can use AI to empower your DevOps teams and talk about the challenges that stand between you and more efficient DevOps processes. 

Ready to optimize? 

Discover how you can integrate AI into your DevOps ecosystem…

What is DevOps and How is AI Changing It? 

DevOps refers to the set of processes and tools that aid your efforts at software development. It combines two essential aspects of software development: deployment and operations. This concept has been used to streamline software development since the late 1980s and the early 1990s. 

The umbrella term covers all processes that ensure the fast deployment, efficient execution and quality operation of enterprise software. 

While DevOps itself is concerned with increasing the pace of iterative software development, the use of advanced AI is taking it to the next level. 

Simply put, AI in DevOps is a way of writing, testing and deploying code for accelerated software development. 

Here are some of the most popular tools that show us how to use AI in DevOps: 

• Machine Learning

ML is concerned with detecting and learning patterns in historical code. It can help DevOps by running performance tests, root cause analysis and security checks. 

• Natural Language Processing

NLP is the AI process that learns how humans communicate. In DevOps, its meaning shifts towards understanding the syntax of specific programming languages. 

Thus, it can provide code suggestions to DevOps beginners and professionals so they never have to begin coding from scratch. 

• Computer Vision

CV is one of the most exciting innovations in AI for DevOps. It helps AI “visualize” the deployment and execution of code in real-time. 

It can be used for performance analysis, model testing, and instant rollbacks for code that doesn’t work as it was envisioned. 

• Chatbots and Virtual Assistants

Chatbots and virtual assistants have become commonplace in today’s world. In DevOps, they can be used to quickly test new lines of code to ensure they’re working as expected. 

They can also be used to help DevOps professionals in efficient troubleshooting and faster query solving.

Advantages of Using AI in DevOps

We have seen that AI has several useful applications in DevOps execution. 

But what benefits can you expect from using AI in DevOps? What are some of the advantages of synergizing AI and DevOps? 

Automation of Routine Tasks

Often, routine tasks are often the most important and dreadful part of DevOps processes. 

For executives, they consume time that could be devoted elsewhere. For developers, it involves working on the most boring and unexciting part of software development. 

So, one of the most important advantages of structuring DevOps with AI is that it automates the routine tasks of software development. 

This will help you:

• Engineer a beta version or minimum viable product as soon as possible and 

• Ensure that your DevOps services company spends most of its time configuring unique features peculiar to your software or application. 

Thus, AI in DevOps is the fastest way of kickstarting your software development process. 

And once it has started, automation will continue to take care of routine tasks, ensuring that your DevOps process remains a creative and iterative pipeline. 

Optimal Resource Management

Since AI is capable of taking on the burden of automating routine tasks like rollbacks and performance testing, your DevOps team can shift its attention to more important tasks, such as planning updates. 

• First, this will help your DevOps team prioritize tasks that require human intervention. 

• Second, it will ensure that there is no compromise on the quality of automated tasks. 

• Third, it will free up your financial and human resources to focus on horizontal and vertical tasks associated with DevOps, such as marketing and sales.

It's a win-win-win situation!

Cost Effective Development

An average company spends from $100,000 to $500,000 in transitioning to a DevOps model. Further, the hourly charges of DevOps professionals range from $20 for beginners to $200 for experts. 

And the average software lifecycle is around 6-8 years. 

This means you will spend at least $100,000 per year on DevOps, not counting the salaries or charges for DevOps professionals on your payroll. 

And if you’re a conglomerate, aspiring multi-national, SaaS or a business that’s looking to scale up, these costs multiply exponentially. 

But not if you use AI for DevOps! Depending on their complexity and scale, AI tools and services for DevOps cost between $5 and $100,000. 

However, an AI tool or service built just for your enterprise through custom AI Development Services can help you save hundreds of thousands of dollars over the lifecycle of your software and its development process. 

Efficient Software Lifecycle

The most important consequence of deploying DevOps with AI is that it will make your software lifecycle more efficient. 

Imagine this: you have rolled out a new update, but it’s not working as you intended. Or, it’s working as intended, but the user response is unfavorable. 

In such scenarios, you can program AI tools for DevOps to restore earlier versions of your code while you work on fixing the problems. 

AI can act on “triggers” such as error detection or a considerable drop in traffic. 

This will free up your DevOps team to work on more strategic tasks, such as understanding the problem and engineering meaningful solutions. 

Improved Code Quality 

Compromising with code quality is a dangerous and challenging theme of DevOps. 

If you operate a customer-facing business, poor code quality can result in poorly executed websites that turn customers away. This could lead to a higher churn rate and, therefore, a loss in revenue. 

If you run a B2B or SaaS enterprise, code quality is your bread and butter. Your high-value clients rely on you for seamless software or a highly-response UI/UX. 

AI in DevOps can help you guarantee consistency in the quality of the code you develop and deploy. It can ensure that your software is developed, deployed and run as smoothly as possible. 

Extra Layer of Security

In the era of AI and fast development, security has become a mounting concern for business owners, development professionals, customers and governments. 

This is evident from the debate between SecDevOps and DevSecOps: security is now a major part of software development. The only question is how to integrate it within the DevOps process.

Here, AI can offer a helping hand to DevOps, SecDevOps as well as DevSecOps. 

It has numerous applications through processes such as error detection, performance assessment and root cause analysis.

When deployed together, AI and DevOps can also help you code security algorithms that can trigger lockdowns to prevent data theft or hacks. 

It can further integrate blockchain, cloud computing and CI/CD to ensure that security patches provide an extra layer of security to your software and, therefore, end users.

Best Practices on How to Use AI for DevOps

So, what should you do if you’re convinced that automation has become paramount for efficient DevOps processes? 

How can a DevOps team take advantage of artificial intelligence? 

Here are some of the best practices you can follow to empower the capabilities of your DevOps team:

CI/CD Continuous Integration or Development and Delivery

CI/CD is perhaps the most important contribution of AI to DevOps. 

This is due to two reasons: 

• First, Continuous Integration and Deployment have become increasingly important in a world where consumers are demanding more and more personalization.
  This means that businesses have to find ways to deliver new features and updates that cater to their consumers' needs and don’t fall behind in deploying the latest features that their users desire.
  

• Second, CI/CD are the functions that most seamlessly match AI's capabilities. This is because AI’s strengths are pattern detection, performance assessment and security enforcement.
  In other words, the way to extract the most value from AI in DevOps is to engineer it to ensure faster CI/CD.

So, if you’re developing an AI DevOps algorithm, your first priority should be to integrate it with your CI/CD processes. 

Pro Tool: Jenkins helps you build a CI/CD pipeline for your software development projects. 

Code Suggestions

Writing the first few lines of code is often the most difficult part of coding. It sets the tone, the structure and the groundwork of your software development. 

Increasingly, people have shifted towards third-party software that allows them to deploy websites, build marketplaces and manage customers. 

But when your enterprise grows, scales up and retains more customers, you might be compelled to develop proprietary software for security, regulatory compliance and even website performance. 

This is where code suggestions come in. AI can help your programmers quickly develop and launch a working version of your software in no time. 

It can also help developers integrate code in languages other than the ones they are familiar with. Thus, you can benefit from sophisticated syntaxes that are suited to specific features. 

AI in DevOps can thus empower you with compatible and multilingual coding. 

Pro Tool: Tabine can provide your DevOps team with the intelligent coding assistance they need to develop innovative software. 

Automated Code Testing

Testing code is one of the most important aspects of the DevOps process.

It helps you ensure that your software is working according to your expectations and intentions. 

There are two types of code testing - end-to-end tests that show the overall operation of the code and unit testing that tests specific blocks of code. 

However, manual end-to-end testing can take up a significant amount of your DevOps team’s time, while what they should be focusing on is unit testing for the most important blocks of your code. 

Thankfully, with AI for DevOps, they can do just that! 

AI can help you develop end-to-end testing processes, so you can rest assured that no line of your code remains untested. 

Pro Tool: Try Selenium, an open-source automated code-testing tool with a playback function. It allows you to author effective tests across most browsers and platforms. 

Performance Assessment

The next step after code testing is performance assessment. 

Every line of code could be improved with time to increase efficiency, improve user experience and expand the capacity of your software. 

With DevOps AI tools, you can automate performance assessment so you know exactly where your software could be improved. 

Based on the automated performance assessment, you can make a timeline for updating inefficient code and deploying enhanced versions of your software. 

Pro Tool: RhodeCode helps you improve the quality and structure of your code. It also gives you centralized control over the software development process. 

Root Cause Analysis

Errors are disruptive and expensive for enterprises that have software as one of their operations centers. Today, that’s most of our businesses. 

Take the CrowdStrike July Incident: it affected airlines, banks and even sports events! 

Sometimes, end-to-end tests are unable to point out the root cause of errors in your code. 

An error in your latest update could be caused by a combination of lines of code or incompatibility between different syntaxes. 

While your DevOps team can set out to solve this problem by unit testing all blocks of your code, your software will have to be rolled back to a more stable version in the meantime. 

AI for DevOps helps you with both of these processes. 

First, it helps you quickly roll back your software to a stable version through CI/CD efforts. 

Second, it helps you run multiple iterations of the code by including and excluding specific blocks of new code to quickly detect the root cause of your errors. 

Thus, one of the best ways to use AI and DevOps together is to develop a root cause analysis tool that reduces downtime and aids in faster error detection. 

Pro Tool: DynaTrace is a cloud-based AI DevOps tool that combines deep learning and security to deliver important software root cause analysis. 

Automated Security Checks

As the CrowdStrike incident shows, security is one of the most important concerns regarding DevOps.

In today’s trust-driven, competitive environment, customers can always find a more secure alternative to most services. 

Thus, it is important that your code is as robust and secure as possible. 

Here, AI can automate and streamline security-oriented processes such as:

• Security patch management, 

• Access distribution and 

• Continuous vulnerability scanning

This helps you ensure that your code remains immune from latent threats that could compromise the privacy or security of your software or its data. 

It also allows your SecDecOps or DevSecOps teams to anticipate risks and roll out more secure versions of enterprise software. 

Pro Tool: Sysdig is a cloud-powered conversational AI security analyst. It provides multi-step reasoning and contextual awareness to accelerate human intervention against security threats.  

Challenges with Implementing AI for DevOps

While using AI in DevOps looks increasingly inevitable, it is not without its challenges. Let’s look at some of the things that may stand between you and AI-driven DevOps. 

Data Quality 

AI models are most robust when they are trained with the most appropriate data possible. 

So, if you have legacy software that is in the process of catching up with the latest features and syntax, it might be better to modernize your legacy software before deploying AI-powered DevOps processes. 

Remember, your AI model is only as strong as the data you feed it. With faulty data, you may not get the best return on your AI investment. 

We recommend that you consult AI developers to understand the data required to train an AI DevOps model before committing to the process.

Skills Gap 

Accelerated advancement of AI is a recent, post-2020 trend. 

Before this period, AI capabilities were latent, and so were AI-capable engineers. 

Further, several developers are in the process of upskilling to catch up with the trend of “AI-Ops.”

Thus, one of the most significant challenges of using AI in DevOps is to find the right partner for AI development. 

Getting Organizational Buy-In

Flowing from the challenge of the skills gap is the problem of getting organizational buy-in.

Instead of welcoming the change with open arms, many DevOps professionals may be shut off from the idea of taking help from AI tools. 

While this is understandable, you must anticipate and work on this problem to ensure that your developers understand that AI is there to help them, not replace them. 

Getting organizational buy-in will enhance the quality of collaboration between humans and machines in your DevOps process. 

Integration with Existing Infrastructure

Another relevant problem is integration with existing infrastructure.

Often, existing infrastructure might be entirely inadequate for the task at hand.  

Take Google: it is building nuclear-powered plans to develop its AI capabilities. 

And while we can’t all build nuclear infrastructure for our AI DevOps projects, a pivot away from existing infrastructure may be necessary. We suggest that you develop proprietary or explore personalizable third-party cloud-based solutions to truly harness the power of AI for DevOps. 

Initial Investment

And, lastly, we get down to brass tacks: how much will AI in DevOps cost you? 

The simple answer: it’ll make you more than it costs. 

Consider this: if the cost of initial investment is $100,000, but it reduces your human resource expenditure and allows you to retain proprietary software throughout its lifecycle, the only recurring cost you face is management and upkeep. 

And this is an insignificant cost compared to the features and advantages of automating several crucial DevOps processes. 

So, you can be sure that your initial investment will come back to your manifold in the near future. 

Supercharge Your DevOps with AI 

The trend of using AI for DevOps is becoming increasingly popular and shows no signs of slowing down. 

In fact, the AI in DevOps market is projected to grow at 24% over the next decade. Its current estimated value is $2.4 billion, which will reach nearly $25 billion by 2033. 

So, it is more important than ever that you don’t fall behind this curve while your competitors keep marching ahead: now is the crucial time to invest in AI-Ops! 

At Green Apex, our upskilled AI operators are equipped and ready to help you lead the AI-in-DevOps revolution. 

We are here to help you assess, develop and deploy AI-driven solutions to lucrative DevOps problems. 

Where others see problems, we will show you AI-powered solutions. 

Our mission is to help you craft a customized, industry-specific solution to emerging DevOps challenges in the age of AI. 

Reach out to our AI Experts and discover how we can help you integrate AI into your DevOps ecosystem!