It is highly likely that you already know that you have the option to summon a chatbots or install an AI assistant within your IDE, which can help you generate basic code and deployment scripts.
Certainly! When you visit a website like ChatGPT and input a request such as “create Python code for task X” (where X is a specific task or an example task like “display contents of a file line by line”), the website will generate the code for you. If you keep your request straightforward, the generated code will be of good quality. Additionally, if you request the code to be generated in Java instead, the website will oblige and produce high-quality Java code.
If you give the chatbots a new command by saying “new chat” and ask it to “create a bash script for deploying a Java app named X with Gradle to a cluster named Y,” it will do so and inform you of the names of the deployment and service YAML files. You can then ask the bot to “generate the deployment file” and it will do that as well. If you want to “parameterize the Java version,” the bot will also make that change. This process of iterative revision can continue until the deployment code, containers, and deployment files are complete. If you haven’t tried this process before, they highly recommend spending thirty minutes going through these steps to see the potential benefits.
While it may not be completely reliable for handling complex systems, we have made significant progress in reducing the amount of tedious work involved in using templates and preconfigured containers. Similarly, with testing, there are now tools available, including chatbots that can automatically generate test scripts and execution code for a given source code. This means that the only remaining step, which has traditionally been the most time-consuming, is reviewing the results. Additionally, AI can assist in security testing by identifying false positives, and in performance or QA testing, it can help identify only the most severe issues as defined by the organization or a larger pool of applications.
The primary challenge they face presently is related to the workforce. The roles and responsibilities that have traditionally been delegated to entry-level employees trained in Dev/Ops/DevOps are no longer essential, as chatbots or AI can perform those tasks. When they consider the implications of this, it’s genuinely impressive and quite exciting.
However, this progress also raises a concern that every organization must take into account. The way they perform tasks and the skills required for them are changing, which is a positive development. However, change always brings uncertainty, and the challenge we must now confront is, “Now that we no longer require entry-level employees to perform these tasks, how do we recruit and train them for other roles?” They do not have a definitive answer to this question yet, as my perspective is shaped by my own time. However, my advice for those interested in DevOps is to learn the fundamentals, such as understanding at least one Linux command shell, how networking works in containers and writing scripts to deploy containers and public cloud instances.
In this opinion, every organization should adopt these tools at a pace that suits them, as it enables us to achieve growth with fewer resources and less pain. However, DevOps must ensure that they are simultaneously training the next generation of DevOps engineers. Even though they have access to high-level tools that can assist us, they will still be responsible for debugging and troubleshooting for the foreseeable future. Therefore, they must have a thorough understanding of the environment and applications. There is a tool available that can describe what the code does in plain English, which is incredibly useful when dealing with complex systems that have many subsystems and complicated code. Although they do not usually mention brand names, this tool can be utilized as a training tool of the future, allowing users to compare code with the AI-generated English description.
At present, even if you do not seek out app description AIs, having entry-level individuals review the files generated by chatbots or AI will serve two purposes. Firstly, it will validate the capabilities of the rapidly advancing AI space. Secondly, it will provide entry-level engineers with exposure to the fundamental aspects of the systems.
Continue to excel in your efforts. Ensure that you have a plan for DevOps in the AI era, but bear in mind that the development of the calculator did not significantly alter the number of mathematicians; it merely increased their productivity. This is precisely what we are doing here, only with more versatility. Ultimately, like a calculator, the AI has an equation to solve and provides a response.
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