Gen AI and DevOps in the same sentence? DevOps has always been about streamlining software development and deployment by interconnecting the development and operations spectrum. Gen AI, or generative artificial intelligence services, on the other hand, is all about creating new content, taking existing resources as a reference based on specialized patterns. The aim is to combine the benefits of artificial general intelligence and DevOps to boost production, automate mundane tasks, and better respond to mission-critical scenarios. Let’s explore how the introduction of Gen AI into DevOps is transforming the already agile and volatile field.

 

What is the true meaning of DevOps Gen AI?

As the term states, DevOps Gen AI is the fusion of generative artificial intelligence into DevOps practices. DevOps at its core is all about collaboration, CI/CD(Continuous integration and deployment), and healthy doses of automation wherever possible. Gen AI, or generative artificial intelligence, on the other hand, focuses on a specific subset of AI that can generate code, analyze data, and predict outcomes.

 

Now, when you combine the two concepts together, you get a futuristic AI-based DevOps practice that automates writing scripts, detection of anomalies, and even shifting infrastructure to meet real-time demands.

 

This futuristic concept of complete automation through AI is far-fetched, but what DevOps Gen AI can do with relative accuracy and stability is provide an intelligent insight. This can allow the team in charge to centre their focus on more innovative solutions rather than brute-forcing bottlenecks faced in operation.

 

What is IAC or Infrastructure as Code?

Before we delve deeper into the topic, it’s best that we lay a foundation upon which those who are uncertain about certain concepts can solidify their reasoning. Infrastructure as Code, or IAC for short, is the backbone of modern-day cloud operations. IAC allows the DevOps team to manage their cloud infrastructure using declarative/imperative code. IAC generally associates itself with tools such as CloudFormation, Terraform, Pulumi, and Ansible.

 

Some of IAC’s key benefits are:

 

Version control through repositories such as Git for better tracking of changes.

 

Automation of infrastructure-based CI/CD

 

Consistency across different stages, such as testing, development, and deployment.

 

Gen AI for DevOps Phases

Text Gen AI can be used within all the phases of the DevOps timeline. In this section, let’s take a brief peek into how the benefits of general artificial intelligence apply at each phase identified. However, it’s important to know that the examples of generative artificial intelligence are not limited to what’s stated below. We are simply noting down the most prominent areas where generative artificial intelligence services are used.

 

Continuous Integration and Continuous Deployment (CI/CD)

 

One of the key benefits of artificial general intelligence within CI/CD is its ability to automate the creation of mock data and unit tests. If the model is properly trained, it can even create its tests by analyzing any changes made to the code. Gen AI can significantly boost both speed and accuracy of deployments by automating the creation of scripts and config files, eliminating the need for human intervention.

 

Monitoring

 

Generative AI has the capacity to analyze incoming data in real-time, which can provide quick insights and real-time alerts. If the model is advanced enough, it can even provide optimizations to improve performance.

 

IAC Management 

 

IAC is everything when it comes to cloud-based deployments, and Gen AI makes the highly stressful process of managing it a tad bit easier. It automates the creation of configuration files to fit the current demands without the need for human intervention.

 

Testing

 

Data never lies, and Gen AI can read all of it! Historical data can be analyzed through AI to detect any potential defects that might go unnoticed by human engineers.

 

What are some DevOps Gen AI use cases?

 

Gen AI DevOps can not only positively impact the system through pipeline enhancements, but also at each and every stage within the agile process. The concept can even benefit certain subsets, such as MLOps, which we will take a glance at through this section.

 

Code Completion 

 

Text Gen AI is known for being able to spin out lengthy codes through prompts, but it can also generate IAC templates! Forget about writing YAML templates and scripts from scratch as a Gen AI can create a boilerplate in mere seconds.

 

E.g.: Create a YAML CloudFormation template to deploy an architecture containing an EC2, DynamoDB, and an S3 bucket, all enclosed within a VPC.

 

Test Automation

 

Conducting uniform and comprehensive tests is a crucial step in the agile process, and Gen AI can expedite it. The Gen AI can analyze your code base and determine test cases that target regression, performance, and functions through the identification of usage patterns.

 

Policy and Compliance

 

DevOps teams can train their Gen AI models on internal policy and compliance frameworks such as HIPAA and SOC 2. This can be of great use when IAC is reviewed, as the AI can pick up any areas where the infrastructure is violating the rules. A flag by the AI is always better than a lawsuit later on through a breach!

 

Documentation

 

Documentation is the bane of all developers! Imagine sifting through thousands of lines of code trying to determine what exactly is going on, when it might not even be your job. Generative AI can make quick work of documentation by effectively summarizing large IAC repositories. 

 

Challenges in current-day Gen AI DevOps

 

Nothing is perfect and never will be, even though striving for it makes all the more sense. There are certain challenges faced when using Gen AI DevOps in an actual production environment. We attempt to highlight these challenges, hoping that newer developments may address them to minimize the risks and complications.

 

Generative models aren’t perfect, and they can cause errors when creating code or generating an analysis. Therefore, it is imperative that we always run a linter to validate outputs or use a human to review results.

 

Gen AI has limited context when compared to an actual human team. This lack of context can negatively affect its generations unless thoroughly informed. Context can range from internal policies, undocumented and documented policies, along with technical details such as existing cloud resources.

 

The Future of Gen AI DevOps

 

Generative AI appears to be a game changer within the field of DevOps, and the future holds so many possible improvements. These improvements could transform Gen AI from simple code generators and testers to AI infrastructure and DevOps agents. Actions such as being able to understand organizational context and automate monitoring and diagnostics could change the dynamics of how Gen AI is being portrayed.