You need to Learn Docker NOW! - It isn't Just for Cloud or DevOps Anymore

There is a huge misconception that if

you want to specialize in AI, machine

learning, data science or become a data

analyst, data scientist, cyber security

professional and all that then you can

forget about the infrastructure and the

infrastructure skills. So basically

forget about the cloud, forget about

automation tools and all that which is

wrong. In this video I will show you why

you need to learn Docker and why you

have to learn it. Now, if you are in IT

or you are considering to be in IT in

the near future or you would like to

have a lot of tools in your skill set

box that you can use and leverage. So,

let's go ahead and ask Chad

GPT. So, let's go ahead and do a simple

search on one of the AI tools available.

Why is Docker valuable and beneficial

to a lot of professions that we are

going to

discuss. But before that, please

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as possible to benefit everyone. So,

let's go ahead and start with what is

Docker? Why is Docker so appealing and

so attractive right now to simplify the

matter, we have software and the

software needs computations or computing

devices, computing power to run, right?

So that could be on a server on a

physical server. It could be on a

virtual machine VMware, HyperV and so on

or it could be on containers or it could

be containerized. It could be run in

software. Basically physical servers and

virtual machines they have issues.

issues like what lack of optimized usage

of resources, slow or long time to

start, slow start um the the fact that

when it is ported to other environments,

it's not guaranteed it will work without

problems. Physical servers, you have to

shift and you have to lift and shift the

server in order to get to another place.

The beauty of containers is the fact

that it's portable, it's highly

scalable, it's very fast to boot and all

that. So let's find out in layman terms

what is a container. So as you can see

on the screen a container we are going

to visualize that as a box. The box

could be as you can see cardboard or it

could be the container the shipping

container on the

ships. So what is the idea of

containers? You need to put everything

that your application or your code needs

to run in one place and lock it.

So you put your code be it Python, be it

Java, R whatever it is and whether you

are working on machine learning on cyber

security or you are a developer, you are

doing automation, you are a devops

engineer, a data scientist, MLOps

engineer, all of that you will need to

write code to do what whatever is

supposed to

happen and the code needs an interpreter

so the computing device or the computing

machine can understand it. That's why we

need the runtime. For example, if this

is Python, we need let's say Python 3

for example to be

installed. Then there would be

configuration parameters like which

version, what is the location of this,

what is the URL of the database you

would like to connect to. All of that is

going to be included as well in the box

in the container. And then you add any

dependencies. You need this driver. You

need that version of this, that version

of that. All of these the passwords

environment variables that you need all

of that is going to be contained and

then we seal the box we seal the

container. So once we do this then all

what we need is to transform that into

an image a template from which we can

run the actual containers. So what is a

container then? The container is nothing

more than a program, a software program

that runs as an isolated process in your

environment. So what's my environment?

That could be a physical server, it

could be a virtual machine. So a

software component that runs on top of

your computing device. If you noticed

here, we haven't mentioned anything

about operating systems. We didn't say

that we're going to add Windows or Linux

or Mac OS or Ubuntu or Real or whatever

the operating system required to run

this. We don't you don't include that in

the container and therefore the features

of the containers that make it very

appealing is it doesn't require a

separate OS. So the size is very small.

You don't need that 40 or 70 gigs for

the operating system to execute and run.

It's isolated. Once we seal it, it

becomes like the shipping shipping

container on the back of the boat or on

the docks of the boats. It doesn't know

anything about the containers around.

The containers around don't know

anything about the content of these

container. So, it's all self-contained

environments and self-sufficient because

they have everything they need to run

because it's small. It's very fast to

run. It's very fast to tear down. We're

talking about milliseconds in this

case. It is repeatable. use the same

template to run container anywhere on

AWS, on Google, on Azure, on premises,

it's going to be the same result and

definitely that also makes it portable.

So today I'm with AWS. I'm not very

happy with AWS. I'm going to ship my

templates elsewhere and I'm going to

move my production workloads elsewhere

as well. And it's easily scalable

software component 60 megs, 60

megabytes, 100 megabytes, 200 megabytes,

half a gig. very easy to scale because

it spins in milliseconds. So this is

very important. But how does this magic

happen and how can I run the code on a

computing device without an operating

system? Doesn't make sense, right? So

let's find

out. So to run the containers, you need

a host and the host can be a virtual

machine or it can be a physical server.

On the host there will be the hardware

required the CPUs the RAM the disks and

the G GCPUs and all that. There is an

operating system that is going to be

shared among the different containers

and the mechanism to share that is

through the engine. This is the

mastermind. This is the brain of the

dockerized or the

containerization. And this is how the

different containers they access the

kernel of your operating system on the

host as if they had their own operating

system. So that's how we succeed in

making

this work this way. All right. So we

know this. Now let's go back and use AI

to tell us how is this beneficial

containerization or docker and learning

docker is beneficial in the different

professions that you could be aspiring

or maybe are working on right now. So

here we go. I asked Chad GPT, would

Docker be beneficial to data analysts,

machine learning engineers, AI

engineers, data scientists, and MLOps

engineers? And the answer was with

examples, Docker is about productivity

boosting, major productivity boost. And

that is why no matter what your

application is, no matter what your use

is, you will need to use that tool to

boost your productivity. So let's take

it with data analysts. The benefit is it

will ensure consistent environments.

Whether you're using Jupyter Notebooks,

Python, R or any database clients, it's

going to provide you consistency

wherever you work and whenever you work.

And here's a use case. running a local

container with a Jupyter notebook that

already has pandas, numpy and other

libraries in Python and a connection to

Postgress or Snowflake. So here's an

example that where the data analyst if

he or she they know how to use Docker

then they can be very productive and

they can be very fast to deploy whatever

they are working on as projects or to

test it. Machine learning engineers

benefit easily isolate model training

environments. You need isolation. So in

that case whether you're using

TensorFlow,

PyTorch, you need to do that isolation

and you need to scale without breaking

the

actual runtime that you are working on.

Use case run GCPU or GPU enabled

training in Docker container using

Nvidia Docker. AI engineers. It will

help them build and deploy deep learning

models with specific dependencies like

the O

andNX or hugging face

and packaging those inside containers.

And here is a use case as well for that

for inference services with fast API or

Flask

plus inside containers and ship it to

staging or to production environments.

Then you have data scientists share

research easily whether you are using uh

full experiments with notebooks and so

on and dependencies they can be

contained and then you can build the

image template and then send it and

share it with others. So this is

reproducible experimentation MLOps

engineers machine learning

ops repeatable automated and scalable

deployments is the core of MLOps like in

DevOps. So Docker is the core of that.

And here's an example. Model service uh

pipelines, CI/CD workflows and mon

monitoring tools all

containerized. So the common thing

across all IT professions that Docker is

going to provide you is environmental

reproducibility. You can reproduce the

environment in development, in testing,

in staging on AWS or elsewhere.

Dependency management. So now it's all

self-contained within that template or

image. So you don't need to worry about

conflicts between different dependencies

for different applications you're

using. Portability, the fact that you

can run it anywhere you would like on

your development environment, on your

laptop, in the cloud, on premises, and

version control because it's all about

scripts and the scripts can be version

controlled. So this tells you why no

matter what the profession that you are

aspiring from the hot skills in IT, you

have to learn Docker. Of course, I did

not include DevOps. I did not include

development. I did not include cyber

security. I did not include cloud

because it's obvious. It's obvious that

they are needed today. This is known.

What is not known is the assumption or

the the misconception that if I'm going

to be in AI or machine learning or data

science then I don't need really really

need to worry about cloud or uh docker

specifically for this one. Of course,

when you scale the containerized usage

or the containerized application

efforts, then going into Kubernetes is

going to be the right step to do. And

probably I'll make another video also

the same style to explain to you why do

you need to do that. So learn Docker and

learn it right now because it's going to

help you master your job no matter what

that is and no matter what your career

path is within it. If you are a

newcomer, if you are transforming into

IT or if you are upskilling into IT,

learn Docker if you haven't done so and

learn it right now. And it's very very

easy to learn. I'm working on a course

that will be published in the next few

days in English and that will take you

from zero to pro in Docker very fast. So

look in the description box for the URL

and I will see you in the next video.

But please consider subscribing to the

channel. activating the notifications

and give us a like and share. Let the

video reach as many people as possible

to benefit immensely. Thank you so much

and I will see you in the next video.