AI For DevOps — Concepts, Benefits, and Tools
Tags: devops, ai, learning, developer
Published: 2024-01-05 06:11:35

**TL;DR** AI is to augment the existing processes and capabilities, it’s not built to replace the sapiens. The jobs, the responsibilities, and the challenges of [DevOps](https://www.hatica.io/blog/what-is-devops/?utm_source=dev.to&utm_medium=referral) are not going anywhere. The increasing inference of **AI in DevOps** is only going to unlock higher productivity for DevOps teams. Generative AI is already enhancing the efficiency of both developers & operations teams. The crux of _AI-led DevOps_ is that it will improve [DevOps effectiveness](https://www.hatica.io/features/dora-metrics/?utm_source=dev.to&utm_medium=referral), and _speed up the DevOps adoption, DevOps toolchain integration, and implementation of [DevOps Best Practices](https://www.hatica.io/blog/devops-best-practices/?utm_source=dev.to&utm_medium=referral)_. Lastly, as AI gels with the DevOps ecosystem, it will lower the entry barriers for developers to explore the realms of DevOps, thus attracting more talent, and enabling more innovation.

## **Are DevOps Teams & Engineering Managers Really Buying into the AI frenzy?**

[46%](https://about.gitlab.com/developer-survey/) of engineering teams have set goals for themselves to _optimize DevOps processes_ in 2023. You may wonder, how engineering managers are planning to do this. 

Well, [41%](https://about.gitlab.com/developer-survey/) of engineering organizations plan to adopt [engineering management platforms](https://www.hatica.io/blog/why-use-engineering-management-platform/), & dashboards to measure [engineering KPIs & metrics](https://www.hatica.io/blog/engineering-kpis/) to improve [development velocity](https://www.hatica.io/blog/software-development-velocity/) & software delivery efficiency. 

Today, you go to any interactive industry event (not the ones where only the speaker blabbers), and no matter what’s the theme of it, someone will definitely steer the discussion around chatGPT & generative AI. 

In DevOps-focused tech events, it’s common today to hear questions like -

_How will generative AI change DevOps? Will AI take away DevOps jobs? What should be the strategy for adopting AI DevOps tools?_ 

_Which AI tools would you recommend for enhancing DevOps processes?_ … and so on.

Also, you will easily sense in DevOps meetups that there is _pressure on enterprise leaders & **[engineering managers to adopt AI in DevOps. There is a sense of immediacy among organizations to ride the AI wave, leverage AI & DevOps tools, innovate digitally, and march ahead of competitors](https://www.hatica.io/blog/great-engineering-managers/?utm_source=dev.to&utm_medium=referral) to adopt AI in DevOps. There is a sense of immediacy among organizations to ride the AI wave, leverage AI & DevOps tools, innovate digitally, and march ahead of competitors**_. After all, who wants to fall behind? 

Everyone aspires to lead the pack, or at least remain relevant in the market. And it makes sense as well. 

Imagine a decade-old enterprise being ousted from the market by a few months-old newbie startups. Wouldn’t that be demoralizing? Anyway, what’s exciting today is the fact that the majority of us, with unanimity, believe that ‘AI has arrived’. 

People know, **_AI’s no fad_**. For instance, [OpenAI](https://openai.com/) dropped [ChatGPT](https://chat.openai.com/) into the market, and boom, it exploded. Millions swarmed to use it, akin to how moths are drawn to a luminous beacon in the night sky. ChatGPT’s chest-thumping roar on arrival was so wild that even the giants could feel the chill in the spine (Nevermind, [Google](https://www.cnet.com/tech/computing/chatgpt-ai-threat-pulls-google-co-founders-back-into-action-report/)). It reverberated “**Generative AI has arrived, and it’s gonna stay**”. I doubt if you could yet say the same about Cryptos. Can you? Even to date, the majority will shy away from getting into Cryptos, despite all the noise.

In fact, we believe, all the AI frenzy that you see around is just a curtain raiser, the show is yet to begin. The following industry signals further bolsters our belief:

- **24%** have already knitted AI deep into their [DevOps processes](https://www.hatica.io/blog/what-is-devops/?utm_source=dev.to&utm_medium=referral#devops-lifecycle)

- **31%** of engineering teams make use of AI/ML technologies for effective [code review](https://www.hatica.io/blog/painful-code-reviews-killing-developer-productivity/?utm_source=dev.to&utm_medium=referral).

- **37%** already use AI/ML for software testing. The numbers are estimated to double up in a year or two

- **51%** of engineering teams make use of AI/ML to check code hygiene

- **62%** of organizations are already practicing [ModelOps](https://neptune.ai/blog/modelops) — _DevOps for machine learning models lifecycle management_.

In short, if early signals are to be believed, it is inevitable that _AI will penetrate every single industry, and every single vertical of the organizations_.

## **How Generative AI Is Influencing the DevOps Ecosystem?**

Continue reading this insight to understand what impact AI will have on DevOps, what problems it will solve, and a lot more. Basically, the insight answers how generative AI will influence the DevOps ecosystem.

### **1. AI is not here to replace but to augment DevOps**

Do you ever wonder [why DevOps](https://www.hatica.io/blog/what-is-devops/?utm_source=dev.to&utm_medium=referral#why-devops) or _DevSecOps_ even exists? **To ship secure software at speed, and at scale.** AI won’t change that at all.

**AI-led DevOps** simply means embracing ML models in your DevOps process to automate SDLC stages as much as possible, without trading security or sacrificing product features. Basically, AI will help DevOps teams to innovate faster with rapid feedback-led iteration cycles, automated testing, predictable [deployment frequency](https://www.hatica.io/blog/deployment-frequency/?utm_source=dev.to&utm_medium=referral), shorter lead time to change (**LTTC**), and reduced mean time to recovery (**MTTR**). All this while _enhancing the **developer experience**_, and making software development more streamlined & less stressful, if not delightful.

So yes, you inferred it right — the core role & responsibilities of DevOps professionals aren’t changing with AI entering the realm. 

DevOps teams will continue to shoulder the responsibilities such as [continuous integration & deployment](https://www.hatica.io/blog/ci-cd-pipeline-tools/?utm_source=dev.to&utm_medium=referral), security, safety, maintenance, resilience, speed, and other key aspects of code/application quality.

AI is not eating away your jobs, but it’s not even like nobody is losing jobs. 

“_Going to the very beginning of the Ops world, because the landscape changes, and the level of abstraction changes, if you get really comfortable using a particular set of tools, and you really stake your professional identity to that, you’re probably always going to be at risk_”, says **Forrest Brazeal**, Head of Developer Media @ Google Cloud. 

Forrest is of the view that it’s more important than ever to learn to code. 

[Generative AI](https://en.wikipedia.org/wiki/Generative_artificial_intelligence) tools can easily spit a tremendous amount of code, but someone needs to read, understand, debug, and maintain this code. So, **generative AI-led DevOps** is not a threat. Instead, on the contrary, **good DevOps engineers can leverage the speed that generative AI unlocks to create higher value to impact the business's bottom line.**

In a nutshell, 

- New layers of abstraction in an **AI-led DevOps** world are inevitable. 

- Roles won’t be wiped out, but they will evolve.

- AI is not a panacea, but the DevOps processes will definitely get accelerated with AI capabilities.

- **DevOps professionals will be able to speed up the process of completing templated tasks** like -
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… possibly, all this can be completely automated with AI.

### **2. Reimagining Complex Workflows with AI**

DevOps’ success is directly proportional to the level of automation you can achieve across the [SDLC stages](https://www.hatica.io/blog/unthreading-sdlc-process/?utm_source=dev.to&utm_medium=referral). Basically, the more you can automate repetitive stuff in the software development life cycle, the higher the efficiency you’ll unlock. That’s exactly what AI does — **automation of repetitive tasks**.

AI tools for DevOps can help [reduce MTTR](https://www.hatica.io/blog/mean-time-to-recovery/?utm_source=dev.to&utm_medium=referral), improve code quality, simplify [code documentation](https://www.hatica.io/blog/code-documentation-practices/?utm_source=dev.to&utm_medium=referral), and improve interoperability. AI-powered DevOps _speeds up integrations, optimizes application performance, reduces operational costs, and implements security at scale._

And that’s why, engineering managers have high hope for **AI & DevOps-led** engineering workflows.

AI is no panacea, but it can definitely help with the slow, manual, complex, and error-afflicted DevOps & SDLC processes that delay the software release cycles. 

In simpler terms,

- Generative AI can help with code development at speed & scale. It can autocomplete your code blocks, or generate code for you based on your natural language inputs/instructions.

- You may also leverage [AI tools](https://www.hatica.io/blog/ai-tools-for-developer-productivity/?utm_source=dev.to&utm_medium=referral) for DevOps to speed up code reviews, identify bugs, and security vulnerabilities, and resolve any code quality-related challenges. For instance, ML models can easily spot software development issues like [resource leaks](https://en.wikipedia.org/wiki/Resource_leak), [buffer overflows](https://owasp.org/www-community/vulnerabilities/Buffer_Overflow), [concurrency race conditions](https://stackoverflow.com/questions/34510/what-is-a-race-condition), [thread starvation](https://docs.oracle.com/javase/tutorial/essential/concurrency/starvelive.html), data inconsistencies, or wasted CPU cycles.

- And of course, you can use AI tools for DevOps to not only automate the generation of test scripts (code), but also to automate software testing, security testing, test data creation & management, and making sense of test results data.

- Besides, _AI tools for DevOps_ also help with infrastructure orchestration ([GitOps](https://www.hatica.io/blog/what-is-gitops/?utm_source=dev.to&utm_medium=referral)), monitoring & alert, and infrastructure resources cost optimization.

Though actual AI-driven DevOps lifecycle may vary based on the engineering culture & DevOps maturity of your organization, on a high level, the evolution of existing DevOps processes may unfurl as the following:

**AI-powered Continuous Integration & Continuous Deployment (CI/CD) pipelines transcends beyond automating building & delivering artifacts to production.**

AI-led CD platforms pull time-series & events data, logs, and errors from business & engineering KPIs & metrics tracking tools through webhooks/connectors/HTTP assertions. Next, the ML models feasts on this data to unsurface any anomalies around _deployment, performance, and reliability._ 

If anything critical is spotted, AI-powered CD tools can **alert DevOps teams for manual intervention**, or even auto-resolve issues with **corrective actions** based on pre-defined standard operating procedures for known issues. For instance, **AI can roll back build artifacts** if the impact of an exposed code vulnerability could be significant to the business. 

However, if everything is flawless, AI helps deploy code changes across **cloud & on-premise** environments.

### **3. AI-led DevOps Monitoring and Alerting Systems**

As mentioned earlier, **AI tools for DevOps** proactively detect anomalies in engineering code & data and responds with corrective actions. Furthermore, predictive analytics helps with preemptive measures by utilizing historical platform/application data to forecast potential vulnerabilities in the code.

### **4. NoOps: Scaling, and Automating Infrastructure**

**DevOps** teams can seamlessly adapt to infrastructure usage patterns via AI-driven insights. **AI-led DevOps** analyzes historical data, usage patterns, and performance metrics to anticipate demand & accordingly, provision & scale resources to ensure optimal performance and responsiveness with minimal human intervention. 

### **5. Automated Configuration Management, Security, and Compliance Assessments**

AI-governed infrastructure configuration & orchestration means **reduced downtimes, improved trustworthiness, increases reliability**, and ensures **uniformity in release management**. When the self-healing AI systems detect inconsistencies (unusual data patterns) in configuration, or slight drifts in security or compliances, these AI systems perform **root cause analysis** and accordingly introduce corrective actions (intelligent rollback & recovery), and restore self to optimal configurations, optimizing schedules and minimizing disruptions. 

However, human expertise remains indispensable for **strategic decisions, ethical considerations, and tasks demanding manual intervention.**

## **Why DevOps Practitioners Must Embrace AI?**

As an [tech lead or engineering manager](https://www.hatica.io/blog/tech-lead-vs-engineering-manager/?utm_source=dev.to&utm_medium=referral) do you actively build your AI-led DevOps tooling system, aka DevOps tech stack? 

Do you understand where those AI-powered DevOps tools can be used in your engineering operations management? 

Also, do you feel bothered by the NoOps fear-mongering, aka a world with no human DevOps professionals? 

Well, complete automation of SDLC, [NoOps](https://www.techtarget.com/searchitoperations/definition/NoOps) is the ideal outcome of DevOps, but that’s way elusive for now. What matters for now is whether you have the necessary skills that DevOps professionals must inculcate/possess to capitalize AI to its best, and subsequently unlock greater agility for teams & organizations.

A no-brainer, of course, **you need to learn using AI tools**. It’s helpful if you understand neural networks ([GANs](https://en.wikipedia.org/wiki/Generative_adversarial_network), transformers), and generic machine learning algorithms & concepts. For instance, [supervised & unsupervised learning](https://www.ibm.com/blog/supervised-vs-unsupervised-learning/), data processing techniques (cleaning, normalization, and feature extraction), ML models & their performance evaluation metrics (Precision, recall, [F-1 score](https://deepai.org/machine-learning-glossary-and-terms/f-score)), etcetera. 

Yes, the learning curve might be steep and time-consuming, but the rewards will be comparatively way more fascinating.

### **Vendor Engineering**

As things get automated, a key factor that would differentiate you from your competitors is how well you leverage AI. Why it is suggested to know the basics of AI & ML because then you can optimally customize & utilize it as per your need.

Going forward, how well-versed you are with the AI & DevOps tooling ecosystem is going to be a game changer for your organization. It can be your new moat because you’ll be able to integrate exactly the right tools from the right vendors into your [SDLC lifecycle](https://www.hatica.io/blog/software-development-lifecycle-best-practices/?utm_source=dev.to&utm_medium=referral) in the right way to maximize value yield while optimizing resource utilization.

In a nutshell, it is more important than ever to **embrace agile principles & AI DevOps tools** into your work culture. 

[Agile principles](https://www.hatica.io/blog/agile-principles/?utm_source=dev.to&utm_medium=referral) like continuous innovation, effective communication & customer collaboration, and continuous delivery of software are already integral parts of the DevOps fabric. 

Teams just need to get more serious about abiding by these principles. Read our insights on What’s DevOps, [Agile software development](https://www.hatica.io/blog/agile-software-development/?utm_source=dev.to&utm_medium=referral), and [Agile Principles](https://www.hatica.io/blog/agile-principles/?utm_source=dev.to&utm_medium=referral) to fully tap into the value when these are fused together. 

Also, you may want to explore the following **AI DevOps tools** that are popular among AI & DevOps practitioners

- [Hatica](https://www.hatica.io/?utm_source=dev.to&utm_medium=referral), to optimize engineering efficiency and enhance developer experience & productivity by analyzing 130+ key [engineering metrics](https://www.hatica.io/blog/metrics-for-engineering-managers/?utm_source=dev.to&utm_medium=referral) pulled from a wide range of SDLC tools your organization uses. It helps reduce your technical debt and improves development velocity.

- [Amazon DevOps Guru](https://aws.amazon.com/machine-learning/ml-use-cases/ai-for-devops/), to detect anomalies & deviations from normal operating patterns to automate root cause analysis & resolution, as well as to help avoid resource outages in the AWS environment.

- [Amazon CodeGuru](https://aws.amazon.com/blogs/aws/find-your-most-expensive-lines-of-code-amazon-codeguru-is-now-generally-available/), the CodeGuru Reviewer analyzes code on pull requests to the attached repositories and suggests improvements in the form of comments. The Profiler helps enhance performance by identifying the most expensive lines of code in terms of vulnerabilities or latency introduced by it and makes recommendations to address the same. Profiler also helps troubleshoot operational challenges in a production environment.

- [Datadog](https://www.datadoghq.com/solutions/devops/), Measures logs, traces, and metrics from every infrastructure component across the organization (including upstream & downstream systems) to help teams visualize real-time data flow between different services and infrastructure components of an application to better understand its infrastructure and dependencies. Helps in implementing automated monitoring solutions to gain improved visibility into configuration management tools and DevOps orchestration platforms.

- [Dynatrace](https://www.dynatrace.com/platform/automation/), is a full stack application & platform observability & security tool. It detects service degradation, security issues, and performance anomalies in auto-trigger remediation workflows. Also helps with alerts and continuous release validation.

For the brevity of this insight, we are omitting some of the names that deserve to be here. Especially the AI testing tools like [Applitools](https://applitools.com/) and SauceLabs that gel well with your CI/CD pipelines. There are tons of other AI tools that aim to help engineering teams with DevOps success, including [Appdynamics](https://www.appdynamics.com/), [NewRelic](https://newrelic.com/), [IBM AIOps](https://www.ibm.com/in-en/aiops), and others.

## **Key Takeaways**

To sum it up, DevOps is headed towards NoOps, but that’s a far, far, far distant future. 

- Right now, companies can concentrate on reaping the full benefits of AI by optimizing their AI & DevOps led SDLC processes to alleviate [technical debt](https://www.hatica.io/blog/technical-debt/?utm_source=dev.to&utm_medium=referral), accelerate innovation, and delight customers with continuous value delivery.

- Organizations must leverage **AI-assisted DevOps workflows** and make them democratically accessible across the organization's infrastructure & application teams.

- Your **AI-led** [DevOps strategy](https://www.hatica.io/blog/multi-cloud-devops-strategy/?utm_source=dev.to&utm_medium=referral) must plan to squeeze in the following benefits:

- **Improve code quality** with AI-assisted code review, bug identification, issue resolution, and code optimization.

- **Automated configuration management & infrastructure orchestration**, performance observation, security monitoring, and alert systems.

- Intelligent & continuous **AI-led software testing**, with automated test script generation, test data generation & management, and analyzing test data results at scale for remediation or improvement of identified issues, vulnerabilities, and performance bottlenecks.
 
There are indeed security concerns about how the data will be utilized by these external **AI DevOps tools**, but that shouldn’t deter you from evaluating & identifying the right AI DevOps tools with the right policies around data governance & utilization, with no known security & IP vulnerabilities/risks.

If you’re not already using it, you may also equip your [engineering teams](https://www.hatica.io/blog/engineering-manager-challenges/?utm_source=dev.to&utm_medium=referral) with Hatica for gaining better visibility into your engineering processes, and to improve your [velocity metrics & engineering efficiency](https://www.hatica.io/blog/velocity-metrics-for-engineering-efficiency/?utm_source=dev.to&utm_medium=referral), as well as enhance the overall [developer experience](https://www.hatica.io/blog/invest-in-developer-experience/?utm_source=dev.to&utm_medium=referral) of the engineers who you employ.

Look for more helpful [engineering blogs on Hatica](https://www.hatica.io/blog/?utm_source=dev.to&utm_medium=referral), and [book a demo ](https://www.hatica.io/demo/)to see how Hatica can help you with engineering sorcery.