Learn MLOps or become obsolete — your choice.

Hello everyone, my name is Abhishank and

welcome back to my channel.

So if you want to get highly paid in IT,

there is a simple success mantra. Learn

the rare skill set. Of course, you

cannot learn any rare skill set, but

learn the rare skill set that has the

potential to grow in the future.

For example, if you go back to 2010s,

everybody was learning development or

QA, but very few people focused on

DevOps and majority of them were system

administrators, build and release

instance who found DevOps relevant. But

what happened in the late 2010s during

the COVID era, companies started

adopting DevOps. Even the startups,

mid-scale organizations, everyone

started adopting DevOps but they could

not find enough DevOps engineers in the

market. So the requirement was high,

demand was high and the supply in the

market was less. Because of that DevOps

engineers were heavily paid in the

market. It went to the extent where some

of the DevOps engineers were paid even

more than the software developers in top

companies.

Now there is a similar opportunity for

MLOps and even LLM ops. Let me explain.

In the last few years you must have seen

companies adopting AI, companies

adopting ML because it has become very

simple for the companies with AI agents

with large language models adopting AI

and ML has become simple. Now ML was

there for a long period of time but in

last 2 years the number of companies

that started building their ML models

has grew almost to 10 times. Now at this

point of time machine learning are still

the ones who are building the models who

are deploying the models and who are

placing the models in the production.

Even when it comes to model monitoring,

it is taken care by the ML engineers.

But as this continues to scale, at some

point companies will realize they need a

dedicated team for it. And that is where

MLOps will come into picture. The same

is with LLM ops as well. Today AI

engineers or ML engineers they are

working on building their own LLMs or

SLMs deploying their own LLMs or SLMs.

But at some point again it can be one

year or 2 years down the line companies

will realize how important is a

dedicated team for MLOps and LLM ops.

And that is when they will start looking

at the market and MLOps or LLMOs will be

heavily paid in the market. So when

there is an opportunity and when you

know that such opportunity will come,

why do you want to ignore? So that's why

in 2025 or in early 2026, make sure you

learn MLOps or LLM ops. Especially if

you are already working as a DevOps

engineer, it is very easy for you to

make this transition because most of the

keywords are same, most of the concepts

are same. Only the implementation of

those concepts is different. For

example, today as DevOps engineer, you

are implementing CI/CD for your

organization. You will implement CI/CD

and continuous training for your models.

The concept is same but the

implementation and the tool chain is

different. So once again if you are

working as DevOps engineers in the

current organization in early 2026 or

late 2025 make sure you spend some time

to learn this rare skill set. If you are

working as system administrators or

build and release engineers or QA

engineers and want to transition to

DevOps, you can still go ahead with it

because DevOps is going to be there till

the time software development or even AI

agents are going to develop the

applications. You will still find the

scope for uh learning DevOps and getting

placed as DevOps engineers. So do not

confuse between DevOps and MLOps. There

are two different sectors or there are

two different uh things. DevOps is for

traditional software development

applications and MLOps is for ML models

or LLM ops is for large language models.

So they are completely different. Now

how do you learn MLOps? Again it's very

simple. You have enough resources on the

internet. Even if you go to our YouTube

channel, we have MLOps playlist where

I've already uploaded seven videos on

MLOps. You can get started for free. And

in future, I'm going to upload a Udemy

course for MLOps which is going to be a

comprehensive MLOps course right from

basics of MLOps to implementing a real

world project related to MLOps. I'll

also share my personal freelancing

experience through that Udemy course.

And coming to LLMOPS, I would say first

start learning MLOps and only then go to

LLM ops because at some point LLM ops is

a subm module of MLOps. Finally, I will

share a detailed road map link in the

description. By going through the road

map, you can understand what concepts

you need to learn to become MLOps

engineer in 2026.

I hope you found this video informative.

If you have any questions, do let me

know in the comment section. See you all

in the next video. Take care.