Automating DevOps with AI & ML

DevOps has revolutionized the world of business, making the process of adding new software to an existing product or service faster and more streamlined than ever before. But incorporating artificial intelligence (AI) and machine learning (ML) into DevOps, businesses could increase that speed and efficiency even further.

This powerful mix can help to automate much of the heavy lifting involved in DevOps, allowing developers and operations teams to focus their time on higher-level tasks and leaving less critical work to computers and robots instead.

Evolution of DevOps

With automated testing, continuous integration, and infrastructure as code, it’s easy to understand why DevOps has become so popular in the last few years. One of the next big steps for DevOps will be integrating machine learning and artificial intelligence into every part of the DevOps process.

The last few years have seen a massive shift in how organizations are adopting DevOps methodologies. It’s safe to say that today, every company has adopted some elements of DevOps, whether they know it or not. According to a recent Enterprise Management Associates (EMA) study, 80% of organizations have incorporated application workflow automation into their DevOps processes.

With automation and self-service capabilities in place, enterprises are rapidly moving to a culture of agile development and deployment. When developers have full control over their code deployments, they can easily take advantage of new updates as they become available.

This level of agility greatly increases their productivity while also dramatically reducing time to market. To complement these new development techniques, businesses are now adopting various AI and ML technologies that help improve overall efficiency across all aspects of their operations.

Also read: DevOps: Understanding Continuous Integration & Continuous Delivery (CI/CD)

Role of AI and ML in DevOps

There are many roles that ML and AI play within an organization, but typically these roles fall into three categories:

• Customer-focused roles focus on providing a better end-user experience for your customers through a better-optimized system. 
• Operational efficiency focuses on making processes and tasks more efficient across teams, so there is less time spent completing menial tasks as well as freeing up developers to build new features and functionality. 
• Compliance focuses on analyzing large datasets of structured or unstructured data to determine if they meet industry standards.

In general, organizations have been primarily focusing on implementing AI and ML at each role to achieve operational efficiencies; however, we will likely see a shift towards using these technologies for both value addition (adding features) as well as removing non-value adding tasks. This can be achieved through either supervised learning algorithms, which use historical data to predict future outcomes, or unsupervised learning, which uses historical data to identify trends in unlabeled variables.

Advantages of Automating DevOps with AI and ML

There are many advantages of automating DevOps with AI and ML. Keep in mind that a fully automated DevOps workflow can reduce cost, increase revenue, and improve product quality and customer satisfaction rates. Some advantages of automating DevOps with AI and ML are:

• Improved visibility into all DevOps and IT processes 
• Increased speed of testing, releasing, and deploying
• Reduced risk of human error from spreading out to production 
• Ability to handle data growth beyond human capacity 
• Reduced manual effort to perform routine tasks 
• Can automate entire service provisioning
• Based on data, ML can help predict errors, and AI can understand the pattern and predict indicators of failure

DevOps is all about integrating development, quality assurance (QA), testing, deployment, and monitoring of a product in order to decrease the time for release. The goal of automating DevOps using AI and ML algorithms is to make a consistent deployment routine, thereby accelerating user releases from weeks or months to minutes or hours without loss of quality or code defects. 

Challenges of Automating DevOps With AI and ML

The potential of AI and ML to automate DevOps processes are well known, but some challenges prevent organizations from successfully automating their DevOps workflows with AI and ML technology.

One major challenge is developing deep learning algorithms for automated DevOps processes. When your automated workflow involves more complex algorithms than simple binary decisions, it can be difficult to fix mistakes if even one variable is incorrect or missing from your input dataset at any given point during execution.

While advances in machine learning allow for smarter decision-making without human intervention or guidance at every step along the process, some algorithms rely heavily on historic data for optimal results. If the system is inadequately trained, then it can produce the wrong results.

Also read: Integrating IT Security with DevSecOps: Best Practices

How ML and AI are Applied to DevOps Culture

AI and ML are essentially human intelligence exhibited by a machine. With ML, these processes are typically achieved through algorithms that allow it to learn from data.

The theory behind these technologies is that more data means a more educated system, which leads to a greater understanding of interactions between software systems in development and production environments. More specifically, they can be used to process large amounts of logs and automatically discover correlations that humans can’t detect on their own.  

AI and ML bring about several benefits for teams, most notably: 

Adaptive systems 

When machines are given access to tons of data and knowledge about different systems, they can make improvements over time without having to be told what needs changing or fixed. For instance, you might not realize that your website gets considerably fewer visitors at certain times of the day because you never noticed certain trends, but an AI would have no problem detecting those patterns after some time has passed. 

Non-human intervention 

Machines don’t need breaks. They can work as hard as possible 24/7 to optimize every detail of a program or service until completion. This allows businesses to handle more business volume than ever before while cutting back on overhead costs that come with running an entire team constantly. 

Stronger testing practices

Adaptive AI and ML create alerts based on other events happening elsewhere in your codebase. That means there will be fewer instances where something falls through the cracks; everything will be covered more consistently since every aspect of your product will be watched closely.

At its core, DevOps is all about collaboration between developers and operations engineers, so they can seamlessly update features together instead of separately. Developers write new functions using API calls to trigger those updates, then operators monitor performance and address problems quickly, so changes can go live within short periods. 

Effectively Using AI and ML Within DevOps Teams

As these technologies continue to mature, they are being applied in new ways by companies from all industries. One such application is in operations management. AI and ML are changing how DevOps is applied. More organizations are adopting these tools to automate data processing, database performance, log file analysis, and more, as AI can solve problems that humans can’t be trusted to solve themselves in terms of scale, time frame, or accuracy.

AI and ML also bring IT into focus faster. From servers to data center operations and application performance, you can use AI to help find anomalies that would otherwise be missed by human beings. And using ML, you can predict bottlenecks before they happen—and then avoid them.

With public cloud computing gaining traction in enterprises, organizations are increasingly looking at automation as a way to improve their IT operations. To accelerate their pace of innovation, they are using AI and ML to automate DevOps in development to production. The basic idea is that moving toward an automated environment will reduce errors, eliminate manual tasks for developers, speed up deployment time for new features or applications, and increase productivity.

Read next: Best DevOps Automation Tools 2022