シャドーイング練習: How to ACTUALLY Get a DevOps Job in 2026 - 動画で英語スピーキングを学ぶ
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What would it take for a complete beginner, someone with no IT job,
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no production experience, and no idea where to start to become somebody that a company trusts with production deployments and infrastructure?
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Is that actually realistic in 2026?
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I think it is.
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I think you can absolutely get there, but you need to understand what the path looks like and what you're building towards.
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You need to understand Linux.
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You need to understand the software development life cycle.
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You need to understand scripting and you need to understand web hosting very well.
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You can learn AWS, Terraform, Docker, Kubernetes in isolation, and that's fine.
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It's useful, but putting those tools together and getting that real experience is what's going to take you to the next level.
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It'll make you ready for the job.
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And I'm going to be honest, going straight from beginner to a DevOps job is something that's not super realistic, unless you find some junior DevOps position where they're willing to train you in,
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which is exceedingly rare nowadays.
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But I don't want this to get you discouraged.
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This does not mean that DevOps is out of reach.
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It means you're going to build your way there.
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By the end of this video, you'll know which first job to pursue, what to learn, what to build, and then when you should actually start applying for DevOps jobs.
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So where should you start?
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In my opinion, you should still start in help desk, tier one support, IT, maybe at an MSP.
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You're going to have to understand how production systems work.
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Then you move towards system administrator or cloud support.
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Maybe you get a cloud engineer job and then eventually you become a DevOps person.
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How long does this take?
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Well, there's honestly no real minimum and it depends where you're at technically and with your experience.
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I think increasing responsibility and exposure matters more than how long you stay in any given position.
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You're probably going to be administering systems first, and then you're going to get that Linux and cloud experience.
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But what if you're already a software developer?
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This is an entirely alternate path.
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From this side, you have to pick up the infrastructure stuff.
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From the operations side, you have to learn Git, GitHub workflows and source control, pull requests, testing and staging environments, build artifacts and CICD.
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And from the software side, of course, you have to learn the infrastructure stuff.
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So either way, you're just learning the other half, essentially.
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Now, what level of Linux do you actually need for this?
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Well, Well, if an application that's running on a Linux server starts breaking, you should be able to troubleshoot using logs without going directly to a tutorial.
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That means being comfortable with Linux users, processes, services, files and permissions,
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packages, logs, SSH, networking, and then environment variables as well.
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If you want something to move towards, I think RHCSA, the Red Hat Certified System Administrator, is a decent certificate to look towards.
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I myself will probably get this at some point.
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If you can't tell, I'm pretty bullish on certifications in general.
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I think they really force you to get the experience needed to do these types of jobs.
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So now you've started administering systems.
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You've worked your way past help desk.
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Now it's time for you to start understanding the software that's running on them.
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You need to understand the entire software development lifecycle.
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A developer starts with an issue.
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They make a branch.
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They commit their changes.
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They open a pull request.
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That code needs reviewing before it's built and deployed.
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Then you need good monitoring and a way to roll back if something goes wrong.
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And then you've got secrets all over the place.
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API keys, tokens, database passwords, usernames, DB URL, all of this stuff.
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Your app is going to need them.
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So is your CI pipeline.
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So are your Kubernetes clusters.
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How you manage these secrets is very, very important.
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I'd use a secrets manager, like in physical.
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Full disclosure, that's the company that I work for.
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But you could use any secrets manager.
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The point is centralizing your secrets allows you to fine -grained scoped access to them.
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You can rotate them from one central place.
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You can sync them to all of these different places, whether that's GitHub, whether that's, you know, your CI pipeline, whether that's your Kubernetes clusters or Vercel or whatever other service you're using.
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Now, this also makes CI and it makes local development so much easier as well.
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The point I'm getting at is learning about secrets management has, to me at least, by proxy, taught me so much about how production infrastructure runs.
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Now, on the note of production infrastructure, one thing that I personally wish I started learning earlier was Kubernetes.
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I would learn Kubernetes as early as you possibly can.
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I was a Windows system administrator.
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I didn't do tons of Linux.
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We had some Linux boxes that we were, you know, kind of managing but it's mostly just updates
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and like easy scripts and stuff but i moved to the startup
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and physical in the infrastructure security space
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and this is like you know dev sec ops adjacent
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and kubernetes was huge it was a big learning curve for
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me i had to spend weeks learning kubernetes i was like
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walking on the treadmill watching videos about kubernetes i was dreaming about kubernetes i was learning about kubernetes secrets
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and how these apps ingest you know secrets
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and where the source of truth should be
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and how external secrets operator works
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and how authentication from your kubernetes cluster to you know your secrets manager works And then that's just secrets management.
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Of course, there's like the entire architecture of how Kubernetes works, right?
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What a pod is, what a node is, how the control plan works, what the, you gotta learn the command line commands, the kubectl commands, I like calling them kubectl commands.
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This is all important stuff.
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And it's stuff that I wish I started learning earlier.
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Because once you start to understand ingress and deployments and services
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and this cluster architecture and really like how all of this high level pieces kind of fit together, I think something clicks in your mind about distributed infrastructure and distributed systems.
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And when you start thinking like this, it makes you more of a systems thinker.
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And this is 100 % necessary if you wanna be a DevOps professional.
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Now there is a cert in the space, as I'm sure you could guess.
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I do wanna get it at some point.
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It's called the CKA, the Certified Kubernetes Administrator.
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There's actually a few of them.
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This is just kind of like the first one, the main one.
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And I really think that this would be worthwhile for you to get, because in order to get the cert, you have to be able to administer, troubleshoot and debug Kubernetes things really fast and
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so it just kind of forces you to be an expert
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once again in this domain now alongside this you need real
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kubernetes projects as well now how would i use ai in
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this entire process well i would definitely be using ai i
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would use it to help me script i would use it to help understand what the scripts are doing understand
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that you don't have to know the exact syntax of the language
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that you're scripting in in order for you to be a
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devops professional nowadays like i'm gonna be honest with you nobody software engineers included or i should say very very, very few are handwriting code letter by letter anymore.
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It's mostly reviewing the output that an AI created.
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Now, this might not be as fun as handwriting code, but I can tell you debugging is a lot more fun
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because you don't have to spend days at a time looking through stack traces and trying to track something down manually.
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So you've got to understand how an API works.
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You've got to understand error handling.
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You've got to understand environment variables.
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You've got to be using AI extensively if you want to be successful in tech nowadays.
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But most importantly, it's the context.
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You need to understand which systems can I and should I automate, whether it's with AI, whether it's with a script, whether it's with N8N or something of that sort.
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In DevOps, you're managing millions, billions of dollars of infrastructure.
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So it's important that you know what you're doing when you're dealing at these different levels.
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You know, if you're doing something locally on your device and just testing something out, you can automate it with AI.
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If it works as you go up this kind of responsibility ladder, we have to have the context about where you can safely run certain things and what the fallback plan is.
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This is huge.
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And this isn't just unique to DevOps.
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This works in IT.
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This works in software development.
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This works for platform engineers.
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This is for everybody, right?
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You have to have context around what you're doing.
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Don't be reckless, essentially, is what I'm saying.
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Now, what project would I build that kind of brings all of this together?
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I would build a small containerized application with a database, put it in GitHub, use pull requests
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and the entire software development kind of process in order to actually merge things into main, set up some CI tests and GitHub Actions, build it, deploy it on Kubernetes,
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you know, set up DNS and HTTPS and host it on real production servers.
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Once it works, break some stuff, mess up DNS, mess up a credential, mess up some setting with the database
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and understand deeply why you've messed this thing up
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and how you've messed it up and how you could fix it in production.
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Now, putting together this project requires a lot of curiosity and conscientiousness.
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Again, you're going to be working with AI to actually set something up for you, but it's quite a lot of work to make up the entire environment and try and mimic a real world scenario and,
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you know, use GitOps and use again, this pull request workflow and understand all of this hosting stuff.
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And then also introduce purposeful problems.
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Like this is a lot of work.
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I'm not going to lie to you.
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And does this count as production experience?
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Of course not.
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You don't have to lie and say that you've managed production Kubernetes clusters before you actually have, but it is experience.
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It's useful experience.
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And through the process of doing all of this work that is going to be painful, you're going to learn a lot of useful stuff
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and this my friends will help you in an interview
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and help you talk confidently about things
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and as i'm doing this i would be writing blogs i
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would be you know making articles i would make a website
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somebody asked this in the youtube comments the other day
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that was like hey how public should i be with my
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learning nowadays being public is as easier as it's ever been
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because you can use ai to help you be public of
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course don't just push out slop nobody's going to like that
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but build things and be curious as much as you possibly can
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and learn in public it's going to help you so you're a complete beginner where do you start i would learn networking, Linux, and Git at a very basic level, and I would start building one application.
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I would still get my beginner level certificates and try and go to, you know, tier one help desk, system administrator, something of that sort, MSP, things like this.
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I would use AI to teach me as much as possible.
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I have a video on Matt Pocock's slash teach.
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It's an AI skill
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that you can use to help AI teach you over a long period of time with some goal in mind.
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It's super, super good.
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Definitely go check it out.
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Matt Pocock is the goat of AI skills.
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Now, when should you start applying for DevOps jobs?
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Again, I would generally say you should start learning things earlier than you think you should.
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So as you're getting your system administrator position, you should be learning Kubernetes.
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You should be learning all of this software side of things.
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You should be learning how infrastructure actually works.
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Of course, you're going to do that in your job as well.
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Once you have a year, year and a half of troubleshooting experience under your belt with just real technical systems, it doesn't have to be distributed systems and all of this insane stuff,
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I would start applying understanding that this application process is going to be a longer application process.
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It's not going to happen overnight.
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But as you have this technical job and you're learning these technical DevOps applicable things, start applying for a job.
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And then also understand what the job is.
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You are going to be a jack of all trades.
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You're going to be managing infrastructure.
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You're going to be up at 3 a .m doing on -call rotation stuff.
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You're going to be the guy or girl who everyone expects to know everything, even though you don't.
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And in today's day and age, working in big tech, things change every two weeks.
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Like legitimately, the thing that you thought was super important two weeks ago might not be super important anymore.
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And there's another thing that you have to learn that's super important.
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This for some people is mentally draining.
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This for me invigorates me because I'm always learning new things and it's super duper fun.
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And I feel like I get to be at the bleeding edge of technology.
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And so this is really cool.
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So yeah, guys, this has been how to actually get a DevOps job from zero in 2026, 2027 seven, I imagine this is still going to carry over.
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I wish you luck.
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If you have any questions, you know, leave them in the comments.
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I read every single one.
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Thank you so much for watching and we'll talk to you in the next one.
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Goodbye.
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この動画で英語学習する理由
「2026年にDevOpsの仕事を実際に得る方法」というテーマの動画は、IT業界の最新動向や専門用語を自然な会話形式で学べる貴重な教材です。スピーカーの話し方はリラックスしていながらも情報量が多く、英語スピーキング練習に最適です。特に、職業的な文脈で使われる表現や論理的な展開を学ぶことで、実践的なコミュニケーション能力が向上します。また、シャドーイングをする際には、速さやイントネーションを真似ることで、自然な発音とリズムを身につけることができます。
文脈の中の文法と表現
動画の中で使われているいくつかの重要な表現を見てみましょう。まず、「What would it take for...」は「...するのに何が必要か」という意味で、疑問文の中で仮定的な状況を表します。次に、「I think you can absolutely get there」の「get there」は「目標に到達する」という比喩的な使い方で、日常会話でよく使われます。さらに、「putting those tools together」は「それらのツールを組み合わせる」という意味で、動名詞句が主語になっています。これらの表現は、専門的な話題を話す際にも柔軟に使えるので、覚えておくと良いでしょう。
発音の注意点
動画の中には、発音が難しい単語やアクセントの変わる単語がいくつかあります。例えば、「DevOps」は「デヴオップス」と発音し、「infrastructure」は「インフラストラクチャー」と長い単語ですが、途中で息を継ぐ場所を覚えておきましょう。また、「troubleshoot」は「トラブルシューティング」と日本語でも使われますが、英語では「トラブルシュート」と短く発音されることもあります。shadowing siteで練習する際には、これらの単語を繰り返し発音して、正確な音を覚えましょう。
この動画を使ってshadow speechを練習することで、英語のスピーキング能力だけでなく、IT分野の知識も同時に身につけることができます。ぜひ、繰り返し視聴して、自然な表現と発音をマスターしましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。