It would be naive to expect DevOps engineers to avoid the artificial intelligence revolution, especially if they were the first to experiment with automation and optimization using any possible tools. Today, AI is a strong ally of DevOps for many reasons.
In this article, we’ll briefly explain those reasons and introduce you to a few tools from our colleagues and our toolbox representing machine learning in DevOps.
From the DevOps perspective, artificial intelligence is the quickest path to automating repetitive tasks like update reminders, the sharp-eyed advisor for infrastructure management, and, broadly speaking, the key to harnessing the ample potential of data within the DevOps ecosystem.
You may find more about the basics of AI in our previous article on the infrastructure aspect.
In 2025, DevOps is a practice, methodology, and set of tools for companies, both enterprises and SMEs, aimed to be efficient with the resources, development, and delivery in their environments. This approach integrates many optimization practices—automation, CI/CD pipelines, microservices, and infrastructure as code—to meet specific business goals. Things we want to have at the end of a day using DevOps are reducing time-to-market, improving operational resilience, and enabling real-time scalability.
One of the most common misconceptions about DevOps is that it exists only within the DevOps team domain. Nope. Gene Kim, author of The DevOps Handbook, aptly puts it: “In high-performing organizations, everyone within the team shares a common goal—quality, availability, and security aren’t the responsibility of individual departments but are a part of everyone’s job, every day.”
For AI-first organizations or blockchain-native startups, DevOps is some kind of gym training that ensures agility in fast-paced innovation cycles of software development. However, it’s not necessary for everyone. For example, industries with legacy systems or those operating on predictable, low-frequency deployments (like small-scale manufacturing) may require less extensive DevOps practices but still benefit from targeted automation and monitoring and alerting solutions.
So, how does the AI in DevOps help with its broad tasks?
So, the union of DevOps and artificial intelligence brings us to various applications. Here, you can review a few examples:
TL;DR
But what does your team think of that? Sometimes, even the most tech-savvy teams try to avoid the implementation of artificial intelligence. Here’s what teams think of AI for DevOps according to the latest StackOverflow Developers Survey 2024:
But all the roses have their thorns. All those bold benefits come at a price. Even being implemented correctly, AI and DevOps tend to stay in a recommendation mode relationship because of the following reasons:
Have you heard about machine learning DevOps biases? Now, they will be your routine.
Artificial intelligence algorithms trained on historical data may perpetuate existing biases, leading to ethical concerns and necessitating careful model training and evaluation.
There’s nothing special about the flow of implementation of this technology compared to all others. You’ve seen that roadmap a thousand times:
But the devil from the details asked us to reveal more AI best practices for your DevOps activities.
When explaining how to implement artificial intelligence in DevOps as a roadmap, providing unobvious yet essential advice can help set your guidance apart. Below are key points, each with a short explanation:
Identify specific, high-impact areas (e.g., anomaly detection in monitoring) and implement artificial intelligence solutions at those areas first. This approach minimizes risk, allows testing and learning, and builds stakeholder confidence.
Not all tasks require AI. You can automate repetitive tasks with scripts while deploying AI for complex problem-solving, like root cause analysis. Prepare for continuous learning, from now on, your self-education will never stop.
Also, schedule periodic reviews of your implementations to ensure they align with current DevOps goals and technological advancements.
Before selecting AI tools for DevOps or models, invest in processes that ensure data is clean, structured, and accessible. Use data engineering to unify disparate data sources and eliminate silos.
Typically, this is the stage where the whole process can slip when, technically, it has yet to start.
Form a team comprising DevOps engineers, data scientists, and domain experts. Upskill them. Make them speak freely. Let them explain everything to each other and the C-level management. Ensure clear communication channels so AI models align with operational realities and business needs, avoiding mismatches between theory and practice.
Many tools can help you better understand artificial intelligence. For example, SHAP (SHapley Additive ExPlanations) can help teams understand why AI makes specific recommendations, fostering trust in the system.
When satisfied with your AI-blended monitoring and automation, move towards DevSecOps. Deploy AI for real-time vulnerability detection or compliance auditing to strengthen the entire CI/CD workflow.
Use pre-trained models and customize them for your specific needs instead of building custom solutions from scratch.
You’ll figure out what amends your model needs on the go. You may also use lightweight models designed for edge AI or frameworks optimized for DevOps environments (e.g., LiteRT or ONNX).
Use sandbox environments to simulate how artificial intelligence will interact with your DevOps processes using accurate data. You can identify bottlenecks, scalability issues, or unintended consequences before live implementation.
Even the best models can fail due to data drift, poor predictions, or unexpected scenarios. To handle AI-driven decision-making errors gracefully, establish fallback mechanisms and escalation protocols.
At last, this is where DevOps and AI come together. We prepare some of our favorite tools and recommendations for the companies or DevOps departments you might be interested in. The whole scope is broader, but the further investigation will be up to you.
All artificial intelligence DevOps solutions tend to be customizable and applicable to practical cases. The one DevOps AI tool we developed by ourselves is PredictKube, and we are proud of how this model changes the way DevOps teams manage their infrastructures with our proactive scaling.
There’s another level of AI application for DevOps, a personal one. And here’s a list of AI DevOps tools our engineers use for their work and troubleshooting:
Most of them make code clear and concise and just help look for answers—things that are no less important than optimization, proactive operations, and automation.
Artificial intelligence tools have made great strides in streamlining DevOps workflows, but fully automating everything still seems out of reach.
There’s just too much complexity, edge cases, and judgment calls that only humans can handle right now. These tools are great for making our lives easier, but they’re more like smart helpers than replacements for real expertise.
