Through automation and delegation, AI can help you take DevOps to a new level
If DevOps — integrating development with operations — upended the world of software development, then the introduction of AI (artificial intelligence) represents a further seismic shift.
With DevOps, the name of the game is automation. DevOps involves Increasing communication and scaling processes, but most of all it’s about speeding up the pace of deployment. And the fastest way to speed anything up is to automate it.
That’s where AI represents a significant advance in the progress of DevOps. AI is, in effect, intelligent automation. It is programmed to replicate human reasoning, decision-making, and skills, and it can be “taught” through machine learning to perform many basic tasks that have been typically managed by humans.
So how can a DevOps team take advantage of artificial intelligence? We’ll explore some of the ways a DevOps team can implement AI, as well as some of the challenges they may face.
AI in DevOps: How to Take Advantage?
AI would seem to be custom-designed for use with DevOps. With a goal of speeding up deployment cycles, improving accuracy, and limiting the amount of time dev teams spend on maintenance tasks, DevOps is ultimately about improving productivity. AI can assist with all of these goals and is itself designed to improve productivity.
Automation with AI
AI excels at performing simple tasks autonomously and is able to process large amounts of data faster than a human. This makes AI for DevOps a valuable tool, given the preponderance of data available to systems operators and the number of necessary basic tasks that must be performed.
One of the fundamentals of DevOps is continuous improvement, or iterative changes to day-to-day processes designed to enhance the application, the development process, or people’s skills. AI can contribute to this process by analyzing data from multiple sources to automatically identify patterns or trends pointing to where improvements can be made. A human can then use these suggestions to decide how to implement them.
Monitoring with AI
Continuous monitoring of systems is an essential component of DevOps to ensure that the rapid pace of deployment isn’t sacrificing uptime. AI can be deployed to monitor logs and other data sources to detect anomalies, allowing human operators time to address issues before they become major problems.
AI can also be employed to monitor cloud usage, pulling data from available sources and analyzing it in real-time to determine whether cloud resource allocation needs to be scaled up or down. This can save critical time when scaling up to meet increasing demand and save money when scaling down to sunset capacity that’s no longer required.
LLMs and DevOps
Large Language Models (LLMs) are generative AI tools utilizing vast stores of data to create predictive algorithms that can answer questions and provide basic support for creative endeavors. LLMs in use by DevOps teams are similar to the well-known ChatGPT, allowing users to submit prompts and get a response generated by AI.
One of the most common uses of LLMs for DevOps is tools that offer code suggestions. Similar to how Gmail will suggest words as you type an email, these AI tools will suggest snippets of code to complete a section as you are typing. They draw from a library of code that has been written in the past to identify snippets that may be of use or may complete the process you’re creating.
AI can also be employed to review code after it has been written. The AI tool will inspect your code using machine learning (ML) to identify any errors or inefficiencies and will recommend changes that can optimize your code’s performance. As with many tasks that AI can perform, this is something a human can do, but AI can do it faster, inspecting massive amounts of code quickly, allowing a DevOps team to focus on more productive tasks.
Enhancing Security with AI
As more and more teams work to integrate security practices into the DevOps workflow to establish what’s known as DevSecOps, it becomes important to analyze how security processes that had previously been pushed to the tail end of development can be integrated into the day-to-day. AI can make contributions to this effort through automation and analysis.
AI tools can be deployed to automate security monitoring, scanning for potential threats and calling them out for human operators to address. Security policy enforcement can also be automated using AI to ensure that security best practices are being adhered to.
Challenges with Implementing AI for DevOps
AI workflows offer many potential benefits, but that doesn’t mean there aren’t any concerns regarding its use. The following are areas of AI usage that should be approached with caution.
Black Box Problem
One of the core issues with AI is that it tends to be a “black box”, i.e. a system that operates without a lot of transparency into how and why it makes the decisions it makes. This can lead to a loss of trust in the AI and an inability to rely on its recommendations. It can also make it difficult to troubleshoot when a problem arises or predict where it might commit errors.
Explainable AI (XAI) attempts to solve this problem by creating AI models with configurable machine learning interfaces so that operators can make adjustments to how the AI “thinks.” This allows operators transparency into the process so that every decision can be explained. Errors can be traced back to why the AI behaved in a certain way, and adjustments can be made so that it does not behave that way in the future.
Data Quality
Another intrinsic flaw of AI is that it relies on an input of massive amounts of data to “train” it how to act and make decisions. While this process is often called “learning,” AIs don’t learn the same way humans do. Humans come to their abilities and ways of thinking over time, through being challenged and educated. As a result, they have a much more nuanced capability for “thinking” and decision-making than AI.
The intelligence of an AI can also be directly linked to the quality of the data it is fed. While a person has the capability of evaluating the data they consume to determine whether it is useful or true, an AI makes no such distinctions. If it is fed false data, it will have false beliefs. The false data will then be perpetuated by the AI, infecting its critical thinking and resulting in inaccurate responses and decisions.
As with many things, you get what you pay for. While there is a seemingly endless array of AI tools available now, you should be discerning when it comes to which AI tools you trust, and the platform partner you rely on.
Over-Reliance
Given the inherent flaws of AI, it’s important to ensure human oversight of critical applications. Over-reliance on AI can lead to errors going unnoticed or bad decisions from the AI percolating throughout the development process. Whatever tasks you automate through AI should include manual checks as part of the process.
Skills Gap
Just as DevOps requires a combination of skills (development and operations), so too does implementing AI into DevOps. Considering both disciplines are relatively new, it can be difficult to find personnel with the necessary skills to work in both arenas. It may be necessary to augment your team or work with consultants who can bridge the skills gap while your team becomes acquainted with the vagaries of working with AI.
Maximize the Potential of AI with DuploCloud
These examples are just a few of the ways AI can maximize the potential of your DevOps journey. With DuploCloud’s support for the popular cloud providers’ services for AI/ML workloads, we can help you explore AI options within the safety and security of our automation platform. With tools like Kubernetes solutions with built-in security guardrails, our platform will allow you to deploy LLMs while ensuring safety and efficiency. And our expertise with AI/ML workflows will allow you to leverage the power of AI while avoiding the pitfalls.
To learn more about how DuploCloud can put AI to work for you, set up a demo today.
