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Tag: Future of DevOps

How Can the Usage of AI Help Boost DevOps Pipelines
With the introduction of AI in the software development lifecycle, you can design, architect, develop, deploy and monitor applications. With AI in the mix, these pipelines can learn, adapt and optimize themselves, redefining software development from start to finish. In this article, we’ll talk about how AI can help make your CI/CD pipelines smarter and efficient.

An Overview of DevOps and Why it Matters

DevOps fosters collaboration, shared responsibility, automation and continuous improvement to improve not just the software but the speed at which software is delivered. DevOps principles are aimed at breaking the silos existing between the development and operations teams, emphasizing shared responsibility, automating testing and deployment processes, forming iterative feedback loops for receiving feedback in accordance with the requirements of the customers, etc.

Why Do We Need CI/CD Pipelines?

Many organizations today adopt DevOps and build software by taking advantage of DevOps practices such as continuous integration (CI) and continuous deployment (CD), which enables their engineering teams to build and ship high-quality software quickly and efficiently. Figure 1 illustrates a typical CI/CD pipeline.

Figure 1: A typical CI/CD pipeline in action

These two practices help you minimize manual overhead and collaboration across all engineering disciplines. CI is about disciplined and regular merging of code changes into a central repository, CD automates the deployment of those changes into production environments, so new features and fixes reach end users quickly and consistently.

The Challenges of Traditional DevOps

AI and ML have been transforming DevOps processes by automating tasks and making them more efficient for development and operations teams, thereby enabling the development and operations teams to work together and deliver high-quality software in shorter release cycles. However, traditional DevOps practices face challenges such as:
- Code reviews, testing, and deployment can be error-prone if done manually.
- Slow feedback loops thereby take more time to resolve issues.
- Limited support for deriving meaningful insights
Using AI in CI/CD Pipelines

In recent times, AI is playing a key role in CI/CD by using machine learning algorithms and intelligent automation to detect errors proactively, optimize resource usage and faster release cycles. With AI, CI/CD pipelines can learn, adapt and optimize themselves, redefining software development from start to finish. By combining AI and DevOps, you can eliminate silos, recover faster from outages and open up new business revenue streams.

Today’s businesses are increasingly leveraging artificial intelligence capabilities throughout their DevOps pipelines to make their CI/CD (an acronym for Continuous Integration/Continuous Delivery) pipelines intelligent, thereby enabling them to predict problems faster, optimize the pipelines if needed, and recover from failures without the need for any human intervention.

There are several ways in which the introduction of AI in CI/CD pipelines can improve automated deployments:
- Improved scalability
- Enhanced reliability
- Better collaboration
- Reduced deployment time
- Faster rollbacks
When you adopt AI into the DevOps practices in your organization, you are applying specific technologies to automate, optimize, and enhance each stage of the software development lifecycle – coding, testing, deployment, and monitoring.

Today’s organizations are using AI in their DevOps pipelines to drive innovation, enabling teams to work seamlessly and achieve rapid development and deployment cycles. The usage of AI can help CI/CD processes in several ways, such as the following:
- Enhanced automation by predicting when a release should be deployed, or deferred if need be
- Enhanced efficiency and quality
- Better support for decision-making
- Improved support for predictive analysis
- Analyzing CI/CD pipelines and predicting issues even before they occur
- Simplifying routing development and testing tasks
- Optimize the usage of infrastructure by analyzing the usage and allocation of resources
- Optimizing test selection, determining which tests should be executed, thereby reducing CI/CD processes and resource requirements
- Perform security scanning, detect patterns that can make the source code vulnerable, automate threat responses, and isolate vulnerable source code
- Improve efficiency by scaling resources up or down dynamically based on demand
How can AI help in DevOps

When you bring AI into your DevOps practices in your organization, you are applying specific technologies to automate, optimize and enhance each stage of the software development lifecycle – coding, testing, deployment and monitoring.

Today’s organizations are using AI in their DevOps pipelines to drive innovation, so teams can work seamlessly and get rapid development and deployment cycles. Bringing AI into your DevOps will give you the best of both worlds – data-driven insights, fast response to market changes and demands. Basically, this will make your organization more efficient, agile and adaptable to market trends, requirements and preferences while generating more business revenue.

How AI helps in DevSecOps

AI can help in DevSecOps in ways such as automating security testing, automating threat detection, and streamlining incident response. You can use AI-powered tools to scan your application source code for security vulnerabilities, automate software patches, automate incident responses, and monitor in real-time to identify anomalies.

AI can reduce the manual time required by your teams to implement security checks, make your development cycles faster, and enable your teams to build and deploy software that is reliable and secure. The AI-powered pipelines will be smarter, faster and more resilient with capabilities such as:
- Intelligent software deployment
- Automated quality & test optimization
- Automated rollbacks
- Build optimization
- Predictive CI/CD pipeline
Future of AI in DevOps Pipelines

In the future, AI and ML will shape the future of DevOps by opening up new avenues for automated decision-making, self-healing systems and intelligent pipeline orchestration. Organizations that can adopt these technologies will be ahead of the curve to build bigger and more capacity-friendly systems that can handle the rapid evolution of modern application requirements.

At a glance, the future of AI-powered DevOps will provide support for the following:
- Autonomous DevOps Pipelines
- Enhanced collaboration
- Advanced Predictive Analysis
- Context-Aware Automation
Conclusion

While automation is the key in CI/CD, before the usage of AI in these pipelines, manual processes were still a part of the SDLC (an acronym for software development life cycle). For example, you may need to deploy a release from the pipeline immediately or hold it for any of several reasons.

AI can help here by reducing or eliminating human intervention, gathering related info, evaluating the changes, predicting good or bad impact, and predicting when to release or when to ship. Long story short — AI-based automation in your CI/CD pipelines can turn the tables for DevOps and add more innovation, efficiency, and sustainability to your CI/CD pipelines.
October 7, 2025
Turning the Software Factory into an Intelligence Engine

We may have just witnessed the closing of one of the most iconic chapters in modern tech. Microsoft CEO Satya Nadella recently stated that the company is transforming from a “software factory” into an “intelligence engine.” This came on the heels of another round of layoffs — with now over 7% of Microsoft’s workforce having been laid off recently — even as the company continues to report strong earnings. The implications of Nadella’s statement are far more profound than just a catchy metaphor for AI adoption. This may be the official end of the era Marc Andreessen launched when he declared “software is eating the world” and anointed every company a “software company.”

Well, here we are. The world has been thoroughly chewed up and digested by software. Mission accomplished. Now what?

The New Mantra: “Every Company Should Be an Intelligence Engine”

We’re entering a new epoch — one where the core differentiator isn’t your software, it’s your intelligence. Not just artificial intelligence, but your organization’s ability to harness data, interpret it in real time, and act — autonomously, contextually and continuously. The software itself, once the crown jewel of digital transformation, is on the verge of being commoditized by the very intelligence it enabled.

Why? Because increasingly, AI is writing the software. GitHub Copilot. Google Gemini Code Assist. Meta’s Code Llama. Even legacy enterprise players are integrating generative AI tooling to auto-generate workflows, scripts, tests and documentation. When AI can write, test, optimize and deploy software faster and better than a team of engineers, you start to ask: Is the code really the product, or is the intelligence behind it what actually matters?

The answer is becoming clear. Software is no longer the endgame — it’s the delivery vehicle for intelligence.

A Mega-Shift That Will Shake Corporate Foundations

This transformation won’t just tweak job descriptions or shift a few budget lines. It’s going to shake corporate foundations like a 9.0 mega-quake.

If your business model depends on proprietary code, you’re in trouble. If your engineering team is still grinding through feature requests by hand while your competitor has deployed autonomous agents to build, test and launch new capabilities in hours — game over.

For large enterprises, the shift to intelligence engines means reorganizing around knowledge, not departments. Data pipelines will matter more than dev pipelines. Insight velocity will matter more than release velocity. The companies that succeed will be the ones that can build, refine, and deploy intelligence continuously — not just quarterly software updates.

For SMBs, this is a once-in-a-generation opportunity. AI-enabled tools are leveling the playing field in a way we haven’t seen since the rise of cloud computing. A five-person startup can now compete with a Fortune 500 in terms of feature velocity, user experience and even customer support. Intelligence, not size, will be the ultimate force multiplier.

And for the much-prophesied “one-person company” — yes, the age is dawning. With agentic AI handling coding, marketing, legal review, customer service and data analysis, a solopreneur can run a digitally native, globally available business without hiring a single employee.

What Does the Path to Becoming an Intelligence Engine Look Like?

This isn’t just about flipping a switch or adding “AI” to your quarterly roadmap. Becoming an intelligence engine is a deliberate journey — and there are signs that mark the way:

1. Data-first mindset: Intelligence engines treat data as the core asset. They prioritize collecting high-quality, domain-specific data, and build the infrastructure to pipeline, label, refine and retrieve it for machine learning and decision automation.

2. Agentic workflows: Static automation is out. Dynamic, self-improving workflows powered by autonomous agents are in. These agents don’t just execute — they learn, adapt and optimize.

3. Continuous intelligence delivery: Think CI/CD, but for insights. Intelligence engines ship insights to decision-makers and operational systems on a real-time basis.

4. Synthetic workforce: Human labor is augmented—or even replaced—by synthetic workers: AI agents, copilots, bots and LLMs embedded into every facet of the business.

5. Organizational un-siloing: The intelligence engine operates horizontally across functions. Sales, marketing, ops and IT share a common data and intelligence backbone.

What Intelligence Engines Can Do That Software Factories Cannot

Let’s be clear: software factories delivered immense value. But they were always limited by human throughput. Intelligence engines are unbounded by those constraints.

An intelligence engine doesn’t just launch a product; it learns from the market response in real time and updates pricing, targeting, messaging, and UX on the fly.

It doesn’t need a quarterly planning meeting to shift direction — it sees signals in the data and pivots autonomously.

It doesn’t just automate workflows; it optimizes outcomes based on multidimensional, real-time feedback.

The compounding advantage of this model is hard to overstate. Companies that reach intelligence engine status will move faster, serve smarter, and scale leaner than anything we’ve seen before.

How Soon Before This Trend Reaches Critical Mass?

We’re already seeing the early adopters — Microsoft, Google, Amazon, Meta — aligning their entire business models around intelligence-first infrastructure. But you don’t need hyperscaler resources to join the club. Thanks to open-source LLMs, accessible vector databases, and SaaS AI copilots, the tools are in reach for nearly everyone.

The next 12–24 months will separate the true transformers from the late-stage digital dinosaurs. The tipping point will come when intelligence engines can fully replace not just manual workflows, but decision-making at scale.

Recognizing the Wave and Riding It

If you’re a technology leader, you need to ask yourself: Are we still building software factories when we should be engineering intelligence?

This doesn’t mean gutting your dev team or replacing your engineers with bots overnight. But it does mean evolving your architecture, culture and processes to put intelligence — not just functionality — at the center of your strategy.

At Techstrong, we’ll be watching this trend unfold across all our platforms — devopsy.kinsta.cloud, Security Boulevard, Cloud Native Now, Platform Engineering,Digital CxO, and of course, Techstrong.ai. We’ll bring you the stories, strategies and signals of the companies making the leap from factory floor to intelligence engine.

Software may have eaten the world — but intelligence is about to rewrite the recipe.

Stay tuned.

July 28, 2025
What Do DevOps Engineers Think of the Current State of DevOps
Over the years, DevOps has been called many things: A culture, a practice, a mindset. Today, it’s increasingly framed as a platform, coalescing with platform engineering, cloud native maturity and next‑generation AI. But what do DevOps engineers themselves think about where we stand in 2025? Having spoken to dozens of professionals, combed through the latest surveys and observed trends closely, here’s a look at what the trenches are telling us.

Platform Engineering’s Domination: A Double‑Edged Sword

Platform engineering has moved from buzzword to battleground. According to a Red Hat survey conducted in September‑October 2024, nearly half of organizations say generative AI is a core component of their platform engineering strategy. And platform engineering maturity strongly correlates with success, particularly when organizations treat their internal platforms as products with measurable KPIs. (Red Hat)

DevOps engineers commonly report relief in daily toil — IDPs and golden paths reduce friction and enable faster delivery. But complaints persist: building and maintaining a platform can be costly, inflexible, and siloed unless it’s scoped carefully. One recent report emphasized that challenges like workflow integration (37%), security risks (37%), and skills gaps (34%) remain barriers even for advanced organizations. (Red Hat)

In short: Engineers appreciate platform engineering — so long as it’s well built, developer‑centric and not a one‑size‑fits‑all. Otherwise, it risks becoming another source of friction instead of productivity.

AI’s Two Faces: Agentic and Generative in the Trenches

Generative AI is no longer a novelty — it’s everywhere in DevOps workflows. Surveys show 83% of organizations plan to increase spending on AI code generation, and 76% on agentic AI in the next 12–18 months. (Red Hat, devopsy.kinsta.cloud) Tooling that automates documentation (76%), generates code (74%) and provides intelligent code suggestions (59%) is already standard fare in many shops. (Red Hat)

But Ai’s influence goes deeper. TechRadar recently identified three generations of AI coding tools: From Copilot‑style completions, to in‑IDE agents, to full SDLC‑integrated agentic systems. As of mid‑2025, tools like GitHub Copilot, DevOps agents and Zencoder Zen are automating entire pipelines — almost like AI co‑engineers. (TechRadar)

DevOps engineers I speak with are cautiously optimistic. They see agentic AI relieving routine toil: Spinning up IaC, reviewing compliance, optimizing cloud costs. Platforms like CloudBees Unify now connect toolchains under AI‑enhanced control planes. (Wikipedia) At the same time, many practitioners note that AI is still brittle — it works, but only when supervised, prompted carefully and monitored.

The rise of “vibe coding” — a nearly free‑flow, conversational style where engineers guide LLMs rather than write every line — is increasingly common in startups, and sneaking into enterprise prototypes. (Wikipedia)

DevOps Tools Maturing into Platforms

The toolchain is consolidating. CI/CD, monitoring, compliance, security and cloud provisioning tools are increasingly bundled or bridged in platform layers. devopsy.kinsta.cloud’s coverage tracks this trend: It’s no longer about separate pipelines, it’s about unified DevOps platforms.

CloudBees Unify is a prime example: Launched in mid‑2025, it unifies governance across toolchains without forcing migration — an AI‑powered operating layer over existing tools. (Wikipedia) Other providers are doing similar strategic shifts, responding to DevOps teams tired of fragmented tooling.

Engineers say platform‑style tools work — when they’re flexible, extensible and don’t lock them out. Simplicity and integration win; fragmentation and vendor lock‑in frustrate.

Cloud Native: From Cutting‑Edge to Default

Cloud Native is no longer optional — it’s the de facto standard. According to surveys, 61% of organizations run some (41%) or most (19%) production workloads on Kubernetes, with AI/ML and analytics topping the list at 56% each. (TechRadar, devopsy.kinsta.cloud) Engineers affirm that masteries like ArgoCD, Flux, service meshes, eBPF and GitOps practices are now standard radiation of skills. (Reddit)

The link between Cloud Native and AI is especially tight; data workloads, model serving and feature rollout are deeply intertwined with container orchestration and microservices. DevOps engineers increasingly identify as “platform engineers” working at scale in that space.

Education and Certification: Catching Up Slowly

DevOps education and certification remain fragmented. Traditional certs — Kubernetes (CKA, CKAD), AWS/Azure/GCP and DevOps Foundation — remain staples. (Reddit) But DevOps engineers express frustration: Formal learning often lags behind real‑world tooling, AI integration, or platform engineering practices.

Many engineers now augment certs with hands‑on labs, bootcamps and informal community learning. Organizations are piloting internal platform engineer training programs to bridge skills gaps. Still, a mismatch persists between the modern tech stack and classroom syllabi.

Macro‑Economics: Jobs, Layoffs and Demand Shifts

In 2025’s turbulent economic climate, DevOps engineers feel mixed pressure. Layoffs in tech surged 35% in Q1‑Q2 2025 versus the same period in 2024. AI is partly blamed, especially in coding jobs.(Business Insider) But experienced practitioners remain in demand. Firms are automating low‑level roles while doubling down on strategic DevOps and platform roles. (Business Insider)

Waze co-founder Uri Levine recently argued that AI will actually increase demand for software engineers — especially those who can adapt, lead, and architect AI‑infused systems. (Business Insider) Meanwhile, others warn that entry‑level roles are shrinking—some startups now operate as “100× engineer” shops, using AI to super‑charge top performers. (Business Insider)

Most DevOps engineers I speak with say: keep growing skills, especially in AI orchestration, security‑by‑design, cloud cost optimization, platform engineering discipline. Senior and strategic roles are safe; apprentice roles are under pressure.

What DevOps Engineers Think of the Road Ahead

Looking forward, DevOps engineers express hope infused with realism:
- AI will accelerate productivity, but not replace engineers. They expect to supervise agents, shape workflows and solve complex problems—not type boilerplate. (Business Insider)
- Platform engineering is here to stay, but success depends on impact, metrics and a product mindset. Engineers expect more rigor in design, developer‑centric UX, service‑like governance and clear business alignment. (adaptavist.com)
- Cloud Native is standard, but emphasis is shifting to stability, cost control, observability and AI‑ready infrastructure. Knowledge of service mesh, eBPF and GitOps is now table stakes.
- Certs matter, but only to a point. Real‑world experience, continuous hands‑on exposure to AI‑augmented tooling and platform building are where skills live.
- Economic uncertainty looms, but so do opportunities — for architects, platform builders, security‑minded DevOps leaders and AI‑savvy engineers.
Final Thought

DevOps engineers today stand at a crossroads: Platform engineering and cloud tooling have matured into the ecosystem, AI is no longer experimentation but embedded flow. Job markets are shifting, but real demand remains strong — for creative, strategic and adaptable engineers who can shepherd tools, teams and AI together into scalable delivery platforms.

From my vantage, DevOps is no longer about “doing DevOps” — it’s about owning DevOps Platforms, empowering developers and leveraging AI as a force multiplier. That’s what the field’s elite expect, and that’s what you should be building toward.

Alan Shimel is Editor‑in‑Chief and Founder of TechStrong. He has been covering DevOps and digital transformation for over two decades, exploring the intersection of culture, tooling and emerging technologies.
July 28, 2025
Predict 2021: What Does the Future Hold?
While 2020 was a challenging year, it was also a year of resilience, innovation and rapid acceleration of digital transformation. So, what does 2021 have in store for us? At Predict 2021 Virtual Summit, taking place Jan. 21-22 at 10 a.m. ET, top industry leaders will share their speculations on what the future holds in the realms of DevOps, cybersecurity, digital transformation, cloud native and IT.

Predict 2021 is a free, multi-track virtual event featuring a conference lobby, presentation theater, expo hall and more. Attendees will be able to interact with sponsors and peers, watch videos and download information. It will be a great opportunity for networking and learning about what the “new normal” will look like for DevOps.

In addition to the great speaker sessions, there will be panel discussions, interactive Q&As with speakers, a scavenger hunt, giveaways and other interactive activities.

This year’s event features outstanding sessions from industry leaders, including:
- “Sustaining The Pace of Digital Change,” keynote by Emmet B. Keeffe III of Insight Partners.
- “Digital Transformation Panel,” keynote by Flint Brenton of Centrify, Ashok Reddy of Digital.ai and Tracy Ragan of DeployHub.
- “Welcome to the Data Care Industry,” keynote by Ron Gula and Cyndi Gula, Gula Tech Adventures.
- “What Does 2021 Hold for DevOps and CI/CD?,” by Jim Rose, CircleCI.
- “Cloud Native Tech,” by Tracy Miranda of CloudBees, Tracy Ragan of DeployHub and Isaac Mosquera of Armory.
- “Software’s Not Living Up To Its Cloud-Enabled Promise,” by Jen Grant, Appify.
- “Observability and AI: Bringing DevOps Back to the Future,” by Adam Frank, Moogsoft.
- “Increase in Cloud-Native Apps Drives Key Challenges and Opportunities in 2021,” by Ankur Singla, Volterra.
On the second day of Predict 2021, MediaOps CEO and devopsy.kinsta.cloud Editor-in-Chief Alan Shimel will also be announcing the 2020 DevOps Dozen² Awards winners.

To check out the full agenda and to register, please visit the Predict 2021 website.
January 12, 2021
Predict 2021: What Does the “New Normal” Look Like for DevOps?

2020 has been pretty tough for most of us, no doubt. It has been a year of uncertainties and unexpected changes, mainly due to the COVID-19 pandemic. However, the future is bright and 2021 is full of possibilities. What does next year have in store for us? At Predict 2021 Virtual Summit, taking place Jan. 21-22 at 10 a.m. ET, top industry leaders will share their speculations on what the future holds in the realms of DevOps, cybersecurity, digital transformation, cloud native and IT.

Predict 2021 is a free, multi-track virtual event featuring a conference lobby, presentation theater, expo hall and more. Attendees will be able to interact with sponsors and peers, watch videos and download more information. It will be a great opportunity for networking and learning about what the “new normal” will look like for DevOps.

Are you interested in sharing some of your predictions for the upcoming year? The call for speakers is currently open — for a limited time, as we are starting to get swamped with submissions from folks who want to share predictions and thoughts on the new year and beyond. Now is your chance to prove your special ability to foresee the future of IT.

We invite all analysts, practitioners, venture capitalists and thought leaders to share their insights, best guesses and predictions for 2021. Abstracts must be submitted through this form by Dec. 11.

For more information, please visit the Predict 2021 website. Sponsorship opportunities are also available. Predict 2021 sponsors include CircleCI, Harness, WhiteSource and more. For sponsorship opportunities please contact Deb Schalm at deb@mediaops.com.

December 4, 2020
DevOps Was Supposed to Kill Developer Jobs. Here’s Why it Didn’t

The rise of DevOps is more an evolution than a revolution, and developer jobs aren’t going anywhere

Development died a long time ago and was replaced by DevOps. Or, at least that was the way it was supposed to happen.

In reality, developers are doing just fine. There are currently almost 150,000 development jobs right now on one major job site, compared with only about 16,000 positions for DevOps. It seems that the revolution in which developers would be replaced with DevOps engineers never actually happened.

In fact, and as we’ve previously argued, many people find that having DevOps on their job title actually harms them when it comes to finding the right job. In this article, we’ll look at why.

The Death of the Developer

The “death” of the developer has been prophesied for quite some time now. Ever since the creation of the concept of DevOps, in fact. The most vocal and prominent instance of this argument was offered by programmer Jeff Knupp back in 2014, who used a blog post to argue that the popularity of DevOps meant that programmers were increasingly unable to focus on what they do best: writing code.

Instead, argued Knupp, the tyranny of DevOps was forcing coders to become generalists rather than specialists. They were being forced, he said, to include on their CVs an ever-growing number of “skill sets,” some of which were pretty tangential to their core role within an organization. Instead of traditional developers, it was feared, an army of “DevOps engineers” would rise who could just about do a wide range of tasks, but do none of them very well.

In the interests of balance, it’s worth pointing out that Knupp—and many who have repeated his argument—have a vested interest in “defending” traditional coders against the rise of DevOps. It might be true that a thorough knowledge of GDPR compliance processes is not technically necessary for a developer, but some of the skills the DevOps highlighted—not least backend security—are best addressed at the development stage. Developers, in other words, shouldn’t be given a free pass when it comes to thinking about the way in which their creations are actually used.

Adapt and Survive

Nonetheless, the popularity of the concept of DevOps has forced many developers to adapt their skill sets, or at least the way they present them. Concepts such as CI/CD, segmentation of internal communication, automation and “continuous everything” have become central to the way many developers work and are highly sought after. We have DevOps to thank for that.

In addition, the rise of DevOps has made room for an ever-expanding set of novel approaches. Five years ago, it seemed that a new approach to IT management was being developed once a month. First came DevOps, then DevSecOps, then QAOps. This explosion of new approaches does seem to be calming down, but they remain influential on at least two levels.

The first is that, although none of these approaches (and arguably even DevOps, as we’ll see below) have developed into distinct disciplines, they have subtly redefined the way in which developers are understood by the average organization. The second is that, in this context, many developers have begun to stress that they have cross-cutting expertise. In other words, they have had to adapt the way in which they present themselves to survive.

The Death of DevOps

Because of this, we should see the rise of DevOps more as an evolution than a revolution.

At the broadest level, DevOps has had a hugely beneficial effect on the process of software development. There is an expectation that developers will have at least a passing acquaintance with the systems and processes that IT teams use. This was not always the case in the past. Developers today have to know how things such as cloud IAM frameworks or APM platforms work, and why they are important for deploying and managing the applications that developers write.

In this context, it seems strange that some coders ever worried that their job would be replaced. While it’s true that developers are now expected to have much broader skills than they did 10 years ago, organizations still need people who can actually code, and will do for decades yet (until we are all replaced with AI, that is).

It seems that some of us were too eager to see a dramatic revolution, whereas the reality has been more subtle and much slower. And indeed, in many cases the “transition” to DevOps has been almost invisible: new graduates are now trained in the basic principles of DevOps, without this ever having become a discrete college course.

Many of these new graduates, in fact, would be hard-pressed to define what DevOps actually is. But that’s not because it’s gone away; quite the opposite. It is now such a fundamental part of the way that most of us do our jobs that it is losing its identity as a separate idea. This means, eventually, that it will not be “development” that dies, but “DevOps” as a distinct addition to it.

The Future

In short, DevOps has not killed the developer, and there is no evidence that it ever will. Some of us, it seems, were being a little dramatic. On the other hand, DevOps has had huge effects on the way that developers work, because it points to the skills that the best developers have. For that reason, there remain tens of thousands of jobs for developers in the conventional sense.

In short, developers work alongside DevOps engineers; they don’t compete with them for the same roles. So instead of seeing ourselves in competition, let’s try a little collaboration.

July 17, 2020
Debating the Future of DevOps

Change happens at an astronomical pace in the DevOps world, and I constantly see organizations shifting in their needs as the weeks and months go by. One week, the most essential need will be a specific configuration management tool and the next, it will be an urgent requirement to work on containerization.

Many companies forget that simplicity often is the key and the focus should be on transferring to engineers the requirements of the business and product owners. It’s not always about implementing every new tool just because another awesome engineering team is using it—they might have different business needs and end goals than yours.

Whether you are consulting for another client or working on your own internal applications, is your focus on how exciting the new container management system is or how to build the best possible end product that the business requires? Your DevOps organization should be focused on understanding these needs as well as the needs of every team across the technology function and more. So how, then, do we find the line between leading, following or resisting trends to create the most competitive advantage for our organizations?

The DevOps movement will continue to break down silos and increase collaboration. I see DevOps and big data coming closer together; for example, our DataOps teams will have the usual deep understanding of infrastructure, the ability to automate processes, write scripts, build CI pipelines and continuously deploy new code. However, there also will be increasing demand to work with the likes of Splunk and Hadoop to process large sets of data as well as monitoring and logging.

The cultural idea of bringing our teams closer together, to understand each other and to increase productivity will drive such shifts in skill sets. Other technology touchpoints such as containerization and serverless architecture will continue to spread deeper into organizations, while security and privacy may creep further up our list of priorities as we continue adopting more technologies into our organizations. However, it may take some tough learning curves before we really understand the implications caused by not taking SecOps seriously.

Every organization has different needs based on the stakeholders, the tech stack, the infrastructure architecture and more. DevOps will continue to shift, take on other aliases and functions, but the truth is that the future of DevOps is simply the future of your organization.

Understanding what that future holds, however, is up for debate. That’s why we’re hosting an event designed to push DevOps thinking beyond current boundaries among practitioners of varying backgrounds. The Future of DevOps Debate, an invitation-only event, is happening Nov. 10. Salt, host of the event, plans to share footage of the event as well as posting further blogs and articles, providing everyone a glimpse into the day’s conversations and, well, debates.

The day includes a dedicated session discusses the pain points, experiences and successes leaders have had in their past and current positions. The day’s conversations will be in debate-style format so that everyone gets their opportunity to share thoughts and contribute. The core focus is to work toward the future of DevOps.

This is the first Future of DevOps Debate, and Salt plans to run a series of events on a quarterly basis. It also contributes to the local New York DevOps community, sponsoring events such as DockerNYC and hosting ServerlessNYC, to name a few.

It’s clear we are at an inflection point in DevOps, as new technologies and processes strive for attention. Together, we can help shape the future of DevOps and push the industry well beyond its current boundaries.

About the Author / Conor Delanbanque

Conor Delanbanque is managing consultant and head of DevOps USA at Salt. Previously, he was supporting and consulting clients across Europe with their DevOps transformation efforts. Connect with him on Twitter at @ConorDevOps.

November 10, 2016