MLOps, AIOps, LLMOps: Explained for People Who Just Crash-Landed into AI
So, you’ve just landed in AI Territory. Maybe you’ve trained a model, been following my newsletter, or joined a team where everyone keeps dropping terms like “MLOps”, “AIOps”, and “LLMOps” like it’s normal.
You’re nodding along like, “Ah yes, of course, the Ops.” But inside? You’re own neural network is having a meltdown.
Let’s fix that.
WTH is With ALL the OPS?
The “Ops” just means Operations. Think of them as the DevOps for AI, each with a different flavor depending on what kind of intelligence you're managing.
If we enter analogy mode, imagine building a robot that can juggle. The fun part is teaching it to juggle. The hard part? Keeping it running every day, monitoring when it drops a ball, updating its firmware, and not letting it accidentally throw one of the balls at someone. That’s Ops.
MLOPS: Machine Learning Operations
This is the toolkit and mindset for taking machine learning models from development to production.
What it involves:
Versioning models and data (like Git, but for brainy stuff).
Training pipelines that don’t break when someone inputs a crazy sentence or special character.
Testing your model like it’s a clumsy 1 year-old (Trust me, I’ve got one).
Monitoring predictions to make sure the model doesn’t go skynet in production.
Think of it as:
DevOps + Machine Learning = MLOps
You’ll see tools like MLflow, Kubeflow, Azure ML, etc., helping wrangle the chaos.
AIOPS: Artificial Intelligence Operations
AIOps isn’t about building AI, It’s about using AI to make IT Operations run smoother.
An easy example on this is to imagine a server getting absolutely mashed at 2:00am. Instead of asking Ben to wake up from his beauty sleep, because he’s on-call, to fix the issue. AIOps can detect what the issue is and auto-fix the problem.
It’s focuses are:
Automating incident detection.
Root cause analysis.
Predictive maintenance for IT systems.
Its pretty much a 24/7 on-call machine, that fixes predefined issues to save human interaction.
LLMOPS: Large Language Model Operations
This one is the latest of the three. Born from the increased use of ChatGPT, Deepseek and other similar services.
LLMOps is all about:
Managing MASSIVE pre-trained models.
Handling prompt engineering and workflows.
Guardrails (So your chatbots doesn’t start roleplaying as a Pikachu in the middle of a customer query)
Deploying and monitoring LLMs safely and to budget.
Realistically its just MLOps but with a turbo boost. With some added complexity around scale, hallucinations and content blockers.
Ok, So Why Should You Care?
If you want to work with AI and not just near it, understanding these “Ops” helps you avoid model mayhem in production, manage AI that doesn’t just work in theory and collaborate better with different engineering teams.
Plus with the rise of AI understanding how the Ops work, future proofs your career as AI moves from cool demos to serious infrastructure.
TL;DR
→ MLOps = Getting ML models into the real world and keeping them alive.
→ AIOps = Using AI to keep IT systems smart, stable, and less stressful.
→ LLMOps = Managing large language models so they don’t melt your GPU or your reputation.
→ If you’re working in AI, you’re probably going to run into all of these, sooner rather than later.
Next Up: 5 AI Tools That Save Me 10+ Hours Every Week (For Real)
I’m quite the AI enthusiast. So, I’ve been testing quite a few different products out there and I’ll be honest, most of them help me out with learning, coding and planning my days out.
Here’s hoping they help you too! This isn’t going to be a sponsored post so its all just personal experience.