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You have cloud basics, that's not enough. Docker deployments, who cares? Kubernetes clusters, everyone is doing that. CI/CD pipeline knowledge, rejected because you need more. A few Terraform templates, not impressive.
Now, these skills were enough for you to get a job in 2022 or 2023. But in 2026, these skills won't even help you get shortlisted. That's the truth. Most people do not want to accept.
The cloud industry has evolved, and the bar has never been higher. Companies now want engineers who understand automation, intelligence, and scalability. Also engineers who can build systems that fix, scale, and monitor themselves.
So if you're still stuck in learning the same tutorials and doing the same projects like everyone else, it is time for you to change direction. Hey everyone, I'm Vishaka and today in this video I'm going to share five cloud skills you need to learn for 2026. I'll also share what they are, why they matter and exactly how you need to start learning them. Let's get into it.
Skill number one, GitOps and platform engineering. So let's start with one of the biggest shifts. Cloud teams are moving from DevOps to platform engineering. Earlier every team used to manually set CI/CD pipelines for every project. Now companies want internal platforms that developers can use by themselves. Similar to self-service DevOps. And this is where GitOps comes in.
In GitOps, everything lives inside Git. Your code, your infrastructure, your configs, your deployments, all of it stays inside Git. And the moment you update anything in Git, it automatically updates the system. And how does it actually work? This happens through tools like Argo CD and Flux CD. Argo CD basically takes everything you push to Git and deploys it to Kubernetes. Flux CD does the same thing but in a lighter and modular way. So in this case, you're no longer running deployments manually and Git becomes the single source of truth.
Now how you can start? First thing is brush up your Git basics. There's a website which is pretty, pretty good for hands-on Git practice. You can find all the resources and links in the description below. Number two is Kubernetes. You need to understand the Kubernetes fundamentals in order to practice GitOps. Once this is done, pick a project on either Argo CD or Flux CD and see how a Git commit becomes a live deployment. You'll feel proud of yourself.
This matters a lot for enterprises because companies need systems which are consistent, reliable, and have minimal deployment errors and GitOps gives you exactly that. Check the description below for the resources in order to learn the stack.
Skill number two, infrastructure as APIs. This is the evolution of infrastructure as code. Now you have already heard about Terraform. It's the most in-demand tool etc etc. But companies these days do not want to write a giant Terraform template to provision their infrastructure. Instead they want to treat infrastructure as programmable APIs. So you define the cloud resource once and the developers can call it anytime when they need it just like calling an API. Tools like Crossplane and Pulumi make it possible.
Now what's the difference? Terraform helps you build cloud resources using config files but Crossplane lets you create the same cloud resources through Kubernetes itself. In this case, Kubernetes is the single source of truth. Just think of it as a control plane. You write the YAML and Crossplane takes it and provisions the cloud resources through Kubernetes. And Pulumi on the other side helps you write infrastructure through languages like Python or TypeScript. So coding is important here. With this approach, infrastructure now becomes dynamic, modular, reusable and versioned just like code.
Now to get started, you do need to understand Terraform and how to write those config files. Number two is you have to understand how APIs work. Then you should also get your hands dirty with Crossplane in a Kubernetes cluster. So my suggestion is to build a project here. Also resources around projects and learning tutorials are in the description below. Now why this matters for cloud teams? They want on-demand infrastructure that scales across projects without rewriting templates every time.
Skill number three, observability and AIOps. Monitoring is not enough anymore and for modern systems you need observability. Observability connects your metrics, logs and traces so you can actually understand what's happening inside the system. Tools like Prometheus for metrics, Grafana for visualization and OpenTelemetry for data collection are much known. Then comes AIOps which adds intelligence on top of observability. As the name suggests, AIOps uses AI to detect issues, spot patterns and predict failures and in some cases fix problems even before a human notices it.
Now how to start? Revisit monitoring basics: logs, metrics, alerts, traces. Number two is get your hands dirty with Prometheus and Grafana. I've also added some projects in the guide below. Then read about OpenTelemetry or get some kind of an idea how it works. And the important one here is understand how AIOps works. Basically, how it analyzes logs and anomalies. If possible, build one project on this.
Now, this matters because every company wants the systems to continuously run with fewer incidents and faster recovery. Incidents may still happen.
Skill number four, AI infrastructure and model deployment. This is my personal favorite. AI is everywhere, but deploying AI models in production reliably and at scale is a whole skill on its own. This is where AI infrastructure engineering comes in. Engineers in this space work with GPUs, model inference, vector databases, model monitoring, latency optimization and scaling workloads across clusters. And you will hear a lot about tools like Triton Inference Server, Ray, KServe. Triton helps in running AI models efficiently on GPUs. Ray helps scale model deployments across multiple machines and KServe provides a Kubernetes-native way to deploy models.
To get started, I'm assuming you already know about Docker and Kubernetes. And now you need to understand how you can containerize an AI model. Then you need to understand GPU basics and scheduling concepts. Third thing is understand inference fundamentals. Basically, how do you serve your models in production? And the final one is you need to deploy a model and track its performance. This is basically the model monitoring step.
Now, there are some amazing courses that you should check out if you want to cover this skill. Make sure you check the guide below. This matters because every company wants to integrate AI and are already integrating AI. So, they need engineers who understand both cloud and AI. There's like so much of AI everywhere.
Skill number five and the final one, event-driven architecture and API intelligence. Modern systems are shifting from request-response models to event-driven workflows. This means applications need to react to events automatically. Whether that's a user action, a data stream, or even an AI output. Tools like Kafka and RabbitMQ make it possible.
Now, Kafka helps you move data between services in real time. And RabbitMQ lets services communicate asynchronously and serverless options like Lambda Cloud Functions take it even further. It basically helps you run small pieces of code whenever an event happens. On top of this, APIs are themselves getting smarter. They apply security logic, run model inference, and make decisions right at the edge.
To get started, you need to get familiar with tools like Kafka and RabbitMQ. If you have heard about event-driven systems, go read about it a little more. Understand asynchronous communication and how it works in distributed systems. There's this one analogy I really like about async communication where basically you drop a message in someone's inbox rather than calling them. They'll read it when they can. So, you don't have to sit on the phone waiting for them. That's basically async. And in order to practice it, look for projects that involve some Cloud Functions or Lambda Functions, any serverless option and work around a little bit on event-driven architectures.
This matters a lot because in 2026, most of the large-scale systems will be event-driven by default. Why? Because it improves performance, reduces cost, and increases reliability.
So these are the five skills that will matter the most for cloud, DevOps, and AI engineers in 2026. If you start practicing this now, you will be ahead of 90% of the industry and be relevant. And these skills are shaping the next generation of cloud platforms.
Now, if you learn two or three of these deeply, you will already be ahead of people who are still learning the basics. Because cloud in 2026 is not about memorizing commands. It's about how well you can stitch automation, intelligence, and scalability together. So, start small. Pick one skill and give it a real effort. By the time others are catching up, you are already ahead for the cloud roles that are shaping the new era.
If you found this video helpful, make sure you like it and also share it with someone who's also preparing for a cloud career. And do comment below which skill you want to learn first. Subscribe to this channel for more cloud roadmaps and guides. And I will see you in the next video.