The rapid advancement of artificial intelligence in recent years has opened up some exciting new ways to optimize our business workflows across various industries. From marketing to software development, nearly every industry today is using AI in some form to streamline their processes for maximum efficiency.

And that includes the field of DevOps too. DevOps, a process designed to improve and optimize the software development process by offering a closer collaboration between development and operations, has seen even greater improvement with the addition of AI.

But the question is – how exactly can modern AI and its future counterparts help organizations achieve DevOps workflow automation; the epitome of DevOps implementation? Join us as we explore the concept and discuss the DevOps avenues that can be improved with the addition of artificial intelligence.

Understanding DevOps – A Brief Look at the Concept

Before we begin, let’s take a refresher on the concept of DevOps. As the name suggests, DevOps is a software development concept where the organization promotes a closer collaboration between the dev and operations teams.

The aim of this approach is to shorten the development time, increase the frequency of deployment, all to deliver a product to the market quicker and of a far higher quality than before. Now, you might be thinking that the description sounds somewhat familiar with Agile development. And that is because DevOps goes hand-in-hand with Agility.

The iterative process of Agile requires DevOps for optimizing efficiency of the development process, especially its focus on optimizing workflow efficiency using automation of operations. With the fluid nature of DevOps, it’s easy to understand why AI can be a great help in identifying problem areas, and optimizing them using artificial intelligence.

In any DevOps implementation today, one or more major AI integrations can be witnessed today, including:

• Generative machine learning models
• Natural language processing
• Computer Vision-based image inputs
• Chatbots and AI-based virtual assistants

In fact, AI can be quite beneficial even for DevSecOps, a variant of DevOps that focuses on integrating security at all levels of software development, in order to avoid releasing code with vulnerabilities. One of the biggest hurdles towards operations optimization is that the operations team is often involved in repetitive tasks.

Those tasks can now be automated in DevOps with better outcomes using the latest AI models, which are designed specifically to the technical needs of software development as a whole. This includes code generation, as well as incorporating different testing solutions to your QA operations. Overall, we can safely assume that artificial intelligence can be a great help with improving efficiency and efficacy of DevOps workflows.

Why Does DevOps Workflow Automation Matter Nowadays?

When we talk about workflow automation in any industry, the aim is to maximize the efficiency of the business, so that they can eliminate as much redundancy as possible. With stiff competition in the industry nowadays, no business can afford to keep valuable resources tied to repetitive tasks, especially when they can be substituted with AI models with smaller overhead costs.

However, with security a constant threat in software development, many organizations have significant issues deciding what workflows should be integrated with AI tools. Many people believe that the addition of AI would only increase the complexity of the security issues. This hinders the organization from achieving true efficiency, but their fears are understandable as the world of modern AI is still relatively new and untested.

Nevertheless, as time moves on, and AI models evolve for the better, we will see more and more organizations using AI automation in DevOps workflows, from monitoring to deployment, and even documentation.

Some of the most common automation needs in DevOps that could use the help of AI include:

• Process Improvement and Enhancement: All processes and operations, no matter if done by man or machine, have room for optimization for maximum efficiency. AI can automate the process improvement operation in your DevOps workflows by analyzing your development processes for bottlenecks or redundancies.
• Predictive Analysis and Issue Monitoring: Real-time analysis and monitoring of the software development process is an inherent part of DevOps. Moreover, with Agility being a popular development concept used nowadays, especially in web development, monitoring for potential issues and predicting their impacts on your product is important. AI can help you streamline this workflow by monitoring it far more efficiently than a human resource.
• Performance Insights Using Business Analytics: As each sprint winds up, or the development project nears its end, understanding what went wrong in order to rectify it for the next cycle/project is important. This can help improve your DevOps efficiency, as well as the quality of your deliverables. AI can help you automate and improve this DevOps workflow as well.

Now, modern AI can help us automate and improve these DevOps workflows, among many others. And as these AI models improve day by day, especially for the software industry, we will soon see AI featuring heavily in many a modern DevOps implementation plan.

How DevOps Automation with Modern AI Tools Help Organizations?

Now that we have established that modern AI is important for various business workflows, including as an automation tool for DevOps, let’s take a look at how some popular AI tools help us accomplish it. Today, you can choose from many commercial DevOps solutions to streamline and automate your DevOps workflows, helping you manage your web development project efficiently, based on your business needs.

The tools we will be looking at will relate to three main DevOps use cases.

Software Development

DevOps is a great entity on its own when we talk about optimizing efficiency in software development. However, the addition of AI into the mix helps you identify bottlenecks and issues with such precision, that it enhances the efficacy of your DevOps workflows even more. The popularity of Kubernetes among the top skills in DevOps shows that automation is quickly becoming a trend in software development.

This includes offering AI-Based solutions that help software developers avoid repetitive tasks by automating them, such as writing repetitive code, or searching for vulnerabilities in the code base. Thus it frees up the actual human resource to focus on other unique and valuable tasks.

Monitoring

Real-time monitoring of development projects, especially web development, offers large amounts of data regarding the health of the systems and resources. Manually sifting through that data to generate important performance insights accurately can be extremely time-consuming. AI can help us by significantly cutting down on the time and human resources spent, thus reducing overhead costs.

Collaboration and Communication

With AI-enabled chatbots and virtual assistants, organizations can improve and automate their DevOps workflows by streamlining communications between various teams involved in the project. from helping find the right data, to efficient organizational knowledge management, AI can be a great automation asset here.

Top AI Tools for DevOps Workflow Automation That Organizations Use Today

Let’s look at some of the top AI-based DevOps tools that can help you streamline and automate your business workflows. Many of these tools are designed to optimize and streamline web development projects, one of the most popular types of software development alongside mobile dev today.

Amazon CodeGuru

Amazon’s CodeGuru is a popular software development tools for those that use the Amazon Web Services. It is designed specifically for DevOps engineers in mind, helping them streamline the software development pipeline using machine learning. CodeGuru helps by analyzing code in real-time and offering improvement suggestions, identifying bugs, and helping improve the overall code quality.

It even helps identify issues such as code readability, resource leaks, concurrency issues, and other similar processes using its highly optimized artificial intelligence. This helps DevOps engineers automate the initial code testing processes usually done by the developers themselves, thus reducing the overall time to delivery.

Sysdig

Sysdig is an innovative analytics platform that allows DevOps engineers to effectively monitor and improve performance in containerized development environments. It uses AI to analyze data patterns, hidden issues, and potential risks and threats within the software development process automatically. Moreover, it uses AI to optimize resource allocation and process improvement, identifying areas of need and suggesting how to boost their efficacy and efficiency.

The tool helps DevOps automate their analytics and monitoring workflows, thus reducing the overhead of a technical analytics resource who would otherwise be needed for critical projects. This also allows the DevOps engineer to focus more on working to implement the changes and improvements suggested, thus reducing time to market for your software product.

PagerDuty

PagerDuty is a popular tool in DevOps, used mostly for its incident management capabilities. Its purpose is to help optimize the CI/CD pipelines, by notifying the team immediately of all incidents that occur during deployments, so that they can take the appropriate action as soon as possible. The aid it offers in helping development teams rectify any negative events such as an unsuccessful deployments quickly helps organizations reduce their time to market, as well as robustness of products.

Its AI capabilities also include automation tools to help them reduce noise, remove manual and redundant tasks from the process, and triage efficiently to prioritize the actions for resolution. Plus, it works right out of the box, without a lengthy and complicated integration process.

Atlassian Intelligence

Atlassian’s relatively new AI-enabled virtual assistant called Atlassian Intelligence offers quick responses to queries with auto-generated answers that sound quite human-like. It works via natural language processing to parse the query, and formulates an exact answer based on that.

It is a great, if limited tool that can help DevOps engineers automate documentation and knowledge management workflows within their organization, thus reducing the time spent on such trivial tasks.

Kubiya

Kubiya is another AI-based virtual assistant designed specifically for DevOps. Like Atlassian Intelligence, it uses a LLM (Large Language Model) throughout the development process. However, it offers a more widespread outcome than the former, by integrating conversational AI to streamline automation.

Its automation capabilities extend into avenues such as insight generation, code generation, team collaboration, deployment, testing, knowledge management, incident management, and more. This frees up the resources including DevOps engineers for higher-level processes that do require a human element to them.

Dynatrace Davis

Davis is an AI-based tool by Dynatrace, designed to monitor and report on system capabilities and status. That includes hardware updates, resource updates, log monitoring, and more. The tool is designed to help DevOps engineers and their IT teams in better monitoring and optimizing resources to manage complex IT environments. It also provides analytics abilities that can help the organization process massive amounts of analytics data independently, quickly, accurately, and efficiently. Thus, its process of automating monitoring and operations is a great asset to any DevOps team.

Modern AI Tools for DevOps Workflow Automation – What Avenues to Focus On?

So, now that we have established that DevOps workflows can be optimized quite significantly using these next-gen AI tools used today, the question is what DevOps avenues most require it. Let’s take a look at three of the key DevOps avenues that can benefit from the addition of AI-driven workflow automation, helping improve operations for tech startups and established businesses alike.

Continuous Integration and Continuous Delivery in DevOps

Continuous integration and continuous delivery ore two of the defining elements of DevOps. By automating the CI/CD processes, you will ensure that your team produces high quality, bug free, and secure software products. Moreover, it will also help you meet your project timelines more accurately, thus reducing penalties and other related costs.

Alongside CI/CD, you can also automate continuous deployment processes, thus ensuring that every change or update the development team makes, is automatically deployed to production.

Software Testing

Software testing is often one of the most time consuming and labor-intensive task in the software development process. Moreover, it is also one of the more critical development processes, which requires great attention to detail.

By automating the process of end-to-end testing in DevOps, you can ensure that you are able to identify all bugs, issues, and software defects as early as possible, so that they are rectified at the earliest. This avoids the issues of rectifying one bug resulting in more bugs popping up down the road.

This automation process will help speed up your software update frequency, which will be a big benefit to your users as well as to your organization.

Process Monitoring

Process monitoring is the last critical DevOps workflow that can benefit from the addition of AI-based automation. Monitoring larger projects can be a time-consuming and resource-demanding task. By automating the process, you not only ensure a more efficient and productive development process, but also a more robust end product by making it easy to track and monitor the development process.

Conclusion

In summation, AI is an amazing tool when it comes to DevOps automation. It allows us to make improve the efficacy of DevOps in general, while ensuring that your software development processes are at their most productive.

However, the process of DevOps automation can be a little tricky and elaborate. But with the right tools and understanding of the important workflows, you can start the journey of automating your DevOps workflows with modern AI tools.

FAQs

1- What are some of the most popular offshoots of DevOps?

Some of the more popular DevOps offshoots include:

• DevSecOps
• AIOps
• CloudOps
• GitOps
• ChatOps

2- How can you incorporate DevOps into your organizational processes?

To incorporate DevOps, you need to follow this basic 5-step process:

• Define your goals for DevOps
• Begin incorporating DevOps processes into high-value areas
• Invest resources in training
• Prioritize and understand the process of improvement and the challenges it brings
• Slowly scale up to extend DevOps to other avenues in the development process

Author Bio: Hailey Savona is an avid content marketer at a leading graphic design agency: Logo Poppin. She loves to talk and write about everything tech including the latest trends. When Hailey isn’t blogging, she likes to take her dogs to the park for a little game of catch.