r/devops • u/hiamanon1 • 6d ago
Discussion Learning in the AI era...
Mid level engineer , who landed and worked my way up into the role. I wont lie and pretend I know everything, some topics I'm still working on, hell I haven't even touched Kubernetes yet, so you can imagine I feel way FREAKING behind.
It seems like everything is quickly adopting and using AI, from downloading models locally, to use and play with them to using them to learn , to using the next leading edge models. How are you all having time for this....
I want to start using AI more confidently but I am not sure where to even start. Local models vs generate chat models via gui in chatgpt or Claude...are there any trainings you guys recommend?
I'm also weary of using AI to learn cause I do know it hallucinates, but also I dont want to feed it and let it spit out answers for me whenever I am just learning how to implement something.
This is developing so fast, I feel like I need to start putting guardrails up for my own sake of learning and sanity, else I will lose knowledge and become a Senior Devops Prompt Engineer lol...jokes aside open to how you are all trying to navigate this, from learning with and without, what guardrails are you implementing , and any learning material would be appreciated.
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u/jabacon75 6d ago
I constantly have to build and learn at the same time. The best way I’ve found to cement my learning is to intentionally reproduce the information AI feeds me.
If I can’t speak or write out the way something works with my own brain then I don’t fully understand it and I know it’s time to read up and try again. It’s been a simple yet extremely helpful practice.
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u/wheresmyflan 6d ago
roadmap.sh has a good AI track that I found helpful
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u/Antique-Stand-4920 6d ago
Much like learning any other technology, it's best to start with a real problem and then figure out how AI could help.
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u/Alarmed-Bank2249 5d ago
You are less behind than the timeline makes it feel. Most people demoing the newest model have not shipped anything with it. The thing worth learning is boring and durable: how to verify generated work - tests that actually assert something, review habits, knowing when output looks clean but is wrong. I work on AI tooling at a bank, and that verification judgment is what separates people who ship with AI from people who collect tools.
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u/Starks1293 6d ago
As others pointed out, I think you just need to understand the key concepts and how it should be wired. I personally learnt a lot from seeing other codebases.
I thought I knew terraform well, untill a company hired another super devops person who built an entire private network across clusters, istio, zero trust auth, warp, dmz zones etc. Only then ive realised how little i knew and my terraform knowledge was crap (was 4-5 years ago).
Now that i know that, and worked on varous codebases i havent seen anything that was better than that yet, it may get more complex but the key concepts are kinda the same
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u/SilverOrder1714 6d ago
I feel a lot of your concerns might go away once you get more familiar with these tools so I would definitely start there.(eg: Anthropic Academy)
The trick is to find a way to incorporate one of these tools (Claude Code, Devin etc) into your daily workflows so that you don’t need to find additional time for it.
The kind of interaction and what the agent is allowed to do (specified in a markdown files) looks very different based on the task I am doing.
- Something which is mundane and offers no learning - the agent is on its own, I do a quick review and it’s checked in mostly as is.
- Something I am learning - we ideate using markdown files, I do the first pass to maximize learning, agent reviews and provides feedback...
You will figure these out for yourself once you start using them.
Happy learning.
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u/TellersTech 2d ago
I use AI pretty heavily in devops now. Terraform, k8s, troubleshooting, code, research, pretty much all of it
I wouldnt really go learn "AI" as a seperate thing though. Just start using it in the work your already doing
For me the rule is basically... if I cant explain what it gave me, or tell when its wrong, I probably shouldnt be shipping it
AI gets me from 0 to 70% really fast. The last 30% is still knowing the actual tech well enough to catch the dumb stuff
Thats kinda where I think the skill is going anyway. Less memorizing syntax, more being able to tell when the AI is confidently full of shit
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u/RumRogerz 6d ago
if you're curious and want to know how everything is actually working, I will read up on what it actually is I'm planning to achieve. This is part of my own personal need to 'know everything'. When my LLM generates code I'll look through it and since I have a decent understanding of what we're trying to produce I _will_ correct it on shit its hallucinating on, what I think it's being inefficient with, and scrutinize the hell out of what it's doing. Just this morning cluade gave me some messy as hell tf code that could have been several lines shorter and coded more efficiently. I also use a lot of md files (https://claude.com/resources/articles/using-claude-md-files) with my own personal guardrails set. For example: 'do not save any code to a file without my express approval'
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u/bonesnapper 6d ago
You already have a job and you're not being pushed to use AI?
Your first step is to obtain a license and a token budget. Do you need to put together a pitch for the buy-in? Use a free product for that.
Second, to learn to use the tool effectively, you should start with the goal of creating a "do my job for me" workflow. Include the admin parts (ticket creation, grooming), the execution, the validation, the communication, and any closeout actions. From there, you'll have a good idea of how capable the tool is and where you'll need to spend some time iterating.
The quality of AI has improved significantly over the past year. Hallucinations won't be lurking around every corner like they were in 2024. The reality is that even the dumb models are far more capable than you, especially if you give them ample context.
I don't think it's useful to spend time with local models or learning how the sausage gets made.
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u/dacydergoth 6d ago
I would start with the theory behind it; learn the math - matrix/vector, gradient descent, back propagation and hyperspacial topology (hypercubes, hyperspheres etc), then learn neural networks and tokenization into latent spaces. Once you have that, you'll have a deep understanding of the basics, and you can move onto training, different neural network layouts, file formats and calculation kernels.
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u/LieutenantNitwit 6d ago
wat
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u/dacydergoth 6d ago
LLMs work by grouping tokens from a parser into similarity vectors. Similarity vectors are vectors in n-dimensional space which form clusters in that space those clusters have a outer boundary which may be described via a hypersphere or bounded hyperspace manifold. LLM weights are a representation of those vectors stored to a file.
Gradient descent is the algorithm used to calculate the weights in the first place, and forward/back propagation is how error value (different between actual and expected result) are fed back during re-enforcement learning.
All this involves a lot of high dimension matrix math, which is done on a CPU or GPU using "kernels" which are a set of pre-weighted matrix calculations.
What's not clear about that?
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u/zen-afflicted-tall 6d ago
matrix/vector, gradient descent, back propagation and hyperspacial topology (hypercubes, hyperspheres etc), then learn neural networks and tokenization into latent space
We talking AI here or Star Wars? I'm ready to jump to hyperspace after reading this comment.
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6d ago
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u/devops-ModTeam 6d ago
Please see the pinned weekly self-promotion thread if you wish to promote your projects or business.
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u/hijinks 6d ago
What I do is when it's something I don't have a understanding of, I ask it to show me docs and references so I can skim those to see if it's correct.
This field is all just building on what you know and growing that understanding.