hey everyone and welcome back to my

channel where we talk about cloud tech

and AI And today we're diving into a

platform that you must know about if

you're doing anything to do with AI It

is Hugging Face Now Hugging Face has

been called the GitHub of machine

learning and for all the great reasons

It is literally becoming the community

where AI models and creations are shared

across everybody So by the end of this

video you will understand what it is

exactly and why it matters to you even

if you are not somebody who codes every

day So stick around All right So this is

Demo

the hugging face homepage And the first

thing that you'll notice in here is

their tagline which is the AI community

building the future That really sums up

what they're really about right it's a

collaborative platform where people are

sharing AI tools models data sets and

even AI apps Now if you scroll down

you're able to see features models that

are trending and recently uploaded

content Um and this gives you the taste

of what's popular in the AI community

And before we dive deeper I would

recommend that you sign up and create an

account because you would need one Uh

most of the content you'll be able to

just see um without uh having an account

but if you want to use the models and

save your favorites and things like that

you will need an account Now um going

into the models tab this is where all

the models are found You can see that

they've got millions of models in here

and you can filter them by different

tasks in the categories like natural

language processing and classification

and audio and tabular

And you could also filter them by

libraries and data sets and languages

and

licenses Now here let's check out u

Microsoft's popular um 54 reasoning

model And I wanted to see how far I can

go with this So each model has got its

own page with documentation and I

clicked on deploy and it literally just

took me right away into the machine

learning studio in Azure AI and I was

asked to create a workspace and as soon

as I gave it all the details with the

name and everything um it was able to

create that workspace for me and

deployed that model the 54 model um from

hugging face into Azure AI machine

learning studio

That was absolutely amazing I just it

just took me a few clicks to do this And

you can see it's creating that now And

once it is created I can go to the

workspace And in this workspace if I

click on endpoints if I click on

endpoints I'm able to go into Azure

OpenAI service And that's where my 54

endpoint is And if I want to use this

endpoint I can click on continue And

that takes me into Azure AI Foundry

where I'll be able to um experiment with

this with this deployed model It tells

me my target URL the key that it created

for me I'm able to see how to use this

with my API key and um some samples of

how to use this model Um I am also able

to go play with it in the playground and

test it out So I gave it a prompt Um and

um I was really trying to go with like

the dog traveling uh to the mountains

where he meets a robot and um that robot

is helping a bird um survive in the cold

um and they all become friends for life

So um I was just playing around with

with um a prompt but the idea here is

that you're able to go from looking at a

model in hugging phase to actually able

to deploy that model in Azure AI found

uh foundry and uh Azure AI machine

learning studio Um and then I clicked on

deploy that um endpoint as a web

application

And right now that is what you're seeing

um with the Azure AI web application

being created um as a part of this

deployment It's able to deploy a web

application right from that um that

model and endpoint that we just created

Once the model this takes seconds maybe

a minute or so to to get created with

the deployment assets and stuff things

like that And once the app is deployed

I'm able to see that app in Azure AI

foundry in my web apps section There it

is the 54 experiment I click on that app

and there we have it An entire chat

application built from hugging face

choosing a model 54 reasoning goes into

Azure AI machine learning studio foundry

and builds it out for me as a web

application Going back into our hugging

Walkthrough

face interface we're able to uh let's

look at the the the data sets tab Now

this tab is where all your data sets are

There are thousands of these in there

and um you can preview the samples in

there You can also filter the data sets

of through languages tasks libraries all

of that Um and then if you click on one

you're able to actually see the samples

of that of the data set um and start

using them Um and now the next thing is

one of my favorites which is the spaces

section of hugging face Now this is

where things get really really exciting

especially for non-coders So spaces is

this interactive AI application that

anyone can use right within the hugging

face browser experience And think of

them as like readytouse AI tools I

clicked on one here which is called

describe anything um in uh by created by

Nvidia And when you go into that model

again right in the browser I'm not doing

anything else I can upload an image And

uh once I do that um I can type my

description and I can get my description

for for the regions of my images This is

my dog sitting on a chair in a park And

um I selected different parts of that

image And um this model is able to this

demo is able to tell me what are in

these different parts Um I selected the

tree first and then I selected my dog

himself and uh it was able to do a

really good job at telling me what is in

this image Um and if I wanted I can take

this space and deploy it for myself

whether locally or um or in cloud Um and

but before we do that let's look at

another example So I go back into my

spaces I can really um you know

categorize by image generation 3D

modeling all the different options up

top Um and I went into stable diffusion

which is another one of the very common

and very popular libraries in gener of

uh image generation models And um I

tested this one out right here in spaces

with a prompt serene lake at sunset with

mountains in the background and a golden

retriever watching the sunset I let it

generate the image And there we have it

Um I don't know if I like the first one

The second one's okay

Um but it it did what I wanted it to do

Um and let's say I'm happy with with

what it's I love the third and the

fourth images Um they really do what I

asked it to do The the good part the

best part the part that I want to show

you is I can run this space Let's say I

like it I can run it locally I can run

it um I can clone the repo um and um and

start working with it right from here

just like how we deployed the um the

five for model in Azure AI and uh with

that um let's look at the docs the docs

section is uh your knowledge center this

is where you are going to get deep

deeper technical information the docs

are organized by different categories

like the client libraries deployment

interface core ML libraries like the

transformers which is one of the very

famous libraries diffusers tokenizers um

and a lot more like radio Um and then

the next thing the last thing I want to

talk to you about is the community

section This is where people ask

questions and share ideas and learn The

blog part of the of the community is

amazing you'll see a lot of people

contributing to the blogs and you'll see

um what's happening right now um and and

what's hot right now Then the learn

section is one of my favorites The LLM

course and the agent course are some of

the best courses out there on AI and

machine learning right now The LLM

course goes from transformers all the

way up to fine-tuning And then the agent

course covers everything from intro to

agents to to a lot more So that my

friends was hugging face and we've

toured every major section of the

platform Whether you are just curious

about AI want to use existing models or

are developing something with AI or want

to contribute hugging face is definitely

a platform to check out Now go explore

And if you liked this video and found it

helpful please hit that like and

subscribe button to get more tech and AI

content And drop a comment if you have

questions and which AI platform I should

cover next And thank you for watching

See you next timehey everyone and welcome back to my

channel where we talk about cloud tech

and AI And today we're diving into a

platform that you must know about if

you're doing anything to do with AI It

is Hugging Face Now Hugging Face has

been called the GitHub of machine

learning and for all the great reasons

It is literally becoming the community

where AI models and creations are shared

across everybody So by the end of this

video you will understand what it is

exactly and why it matters to you even

if you are not somebody who codes every

day So stick around All right So this is

Demo

the hugging face homepage And the first

thing that you'll notice in here is

their tagline which is the AI community

building the future That really sums up

what they're really about right it's a

collaborative platform where people are

sharing AI tools models data sets and

even AI apps Now if you scroll down

you're able to see features models that

are trending and recently uploaded

content Um and this gives you the taste

of what's popular in the AI community

And before we dive deeper I would

recommend that you sign up and create an

account because you would need one Uh

most of the content you'll be able to

just see um without uh having an account

but if you want to use the models and

save your favorites and things like that

you will need an account Now um going

into the models tab this is where all

the models are found You can see that

they've got millions of models in here

and you can filter them by different

tasks in the categories like natural

language processing and classification

and audio and tabular

And you could also filter them by

libraries and data sets and languages

and

licenses Now here let's check out u

Microsoft's popular um 54 reasoning

model And I wanted to see how far I can

go with this So each model has got its

own page with documentation and I

clicked on deploy and it literally just

took me right away into the machine

learning studio in Azure AI and I was

asked to create a workspace and as soon

as I gave it all the details with the

name and everything um it was able to

create that workspace for me and

deployed that model the 54 model um from

hugging face into Azure AI machine

learning studio

That was absolutely amazing I just it

just took me a few clicks to do this And

you can see it's creating that now And

once it is created I can go to the

workspace And in this workspace if I

click on endpoints if I click on

endpoints I'm able to go into Azure

OpenAI service And that's where my 54

endpoint is And if I want to use this

endpoint I can click on continue And

that takes me into Azure AI Foundry

where I'll be able to um experiment with

this with this deployed model It tells

me my target URL the key that it created

for me I'm able to see how to use this

with my API key and um some samples of

how to use this model Um I am also able

to go play with it in the playground and

test it out So I gave it a prompt Um and

um I was really trying to go with like

the dog traveling uh to the mountains

where he meets a robot and um that robot

is helping a bird um survive in the cold

um and they all become friends for life

So um I was just playing around with

with um a prompt but the idea here is

that you're able to go from looking at a

model in hugging phase to actually able

to deploy that model in Azure AI found

uh foundry and uh Azure AI machine

learning studio Um and then I clicked on

deploy that um endpoint as a web

application

And right now that is what you're seeing

um with the Azure AI web application

being created um as a part of this

deployment It's able to deploy a web

application right from that um that

model and endpoint that we just created

Once the model this takes seconds maybe

a minute or so to to get created with

the deployment assets and stuff things

like that And once the app is deployed

I'm able to see that app in Azure AI

foundry in my web apps section There it

is the 54 experiment I click on that app

and there we have it An entire chat

application built from hugging face

choosing a model 54 reasoning goes into

Azure AI machine learning studio foundry

and builds it out for me as a web

application Going back into our hugging

Walkthrough

face interface we're able to uh let's

look at the the the data sets tab Now

this tab is where all your data sets are

There are thousands of these in there

and um you can preview the samples in

there You can also filter the data sets

of through languages tasks libraries all

of that Um and then if you click on one

you're able to actually see the samples

of that of the data set um and start

using them Um and now the next thing is

one of my favorites which is the spaces

section of hugging face Now this is

where things get really really exciting

especially for non-coders So spaces is

this interactive AI application that

anyone can use right within the hugging

face browser experience And think of

them as like readytouse AI tools I

clicked on one here which is called

describe anything um in uh by created by

Nvidia And when you go into that model

again right in the browser I'm not doing

anything else I can upload an image And

uh once I do that um I can type my

description and I can get my description

for for the regions of my images This is

my dog sitting on a chair in a park And

um I selected different parts of that

image And um this model is able to this

demo is able to tell me what are in

these different parts Um I selected the

tree first and then I selected my dog

himself and uh it was able to do a

really good job at telling me what is in

this image Um and if I wanted I can take

this space and deploy it for myself

whether locally or um or in cloud Um and

but before we do that let's look at

another example So I go back into my

spaces I can really um you know

categorize by image generation 3D

modeling all the different options up

top Um and I went into stable diffusion

which is another one of the very common

and very popular libraries in gener of

uh image generation models And um I

tested this one out right here in spaces

with a prompt serene lake at sunset with

mountains in the background and a golden

retriever watching the sunset I let it

generate the image And there we have it

Um I don't know if I like the first one

The second one's okay

Um but it it did what I wanted it to do

Um and let's say I'm happy with with

what it's I love the third and the

fourth images Um they really do what I

asked it to do The the good part the

best part the part that I want to show

you is I can run this space Let's say I

like it I can run it locally I can run

it um I can clone the repo um and um and

start working with it right from here

just like how we deployed the um the

five for model in Azure AI and uh with

that um let's look at the docs the docs

section is uh your knowledge center this

is where you are going to get deep

deeper technical information the docs

are organized by different categories

like the client libraries deployment

interface core ML libraries like the

transformers which is one of the very

famous libraries diffusers tokenizers um

and a lot more like radio Um and then

the next thing the last thing I want to

talk to you about is the community

section This is where people ask

questions and share ideas and learn The

blog part of the of the community is

amazing you'll see a lot of people

contributing to the blogs and you'll see

um what's happening right now um and and

what's hot right now Then the learn

section is one of my favorites The LLM

course and the agent course are some of

the best courses out there on AI and

machine learning right now The LLM

course goes from transformers all the

way up to fine-tuning And then the agent

course covers everything from intro to

agents to to a lot more So that my

friends was hugging face and we've

toured every major section of the

platform Whether you are just curious

about AI want to use existing models or

are developing something with AI or want

to contribute hugging face is definitely

a platform to check out Now go explore

And if you liked this video and found it

helpful please hit that like and

subscribe button to get more tech and AI

content And drop a comment if you have

questions and which AI platform I should

cover next And thank you for watching

The OWASP Top 10 for Large Language Models represents the most critical security risks facing AI applications in 2025. As LLMs become increasingly embedded in applications across industries, understanding and mitigating these risks is crucial for developers and security professionals. In this article let’s go over an AI application architecture covering each of the OWASP Top 10 for LLMs and understand the prevention methods for each.

To understand Model Context Protocol (MCP), let's start with a familiar concept: APIs in web applications.

Before APIs became standardized, web developers faced a significant challenge. Each time they needed to connect their application to an external service—whether a payment processor, social media platform, or weather service—they had to write custom code for that specific integration. This created a fragmented ecosystem where:

• Developers spent excessive time building and maintaining custom connectors

• Each connection had its own implementation details and quirks

• Adding new services required significant development effort

• Maintaining compatibility as services evolved was labor-intensive

APIs (Application Programming Interfaces) solved this problem by establishing standardized ways for web applications to communicate with external services. With standardized APIs:

• Developers could follow consistent patterns to integrate services

• Documentation became more standardized and accessible

• Updates to services were easier to accommodate

• New integrations became significantly faster to implement

MCP addresses the exact same problem, but for AI applications.

Just as APIs standardized how web applications connect to backend services, MCP standardizes how AI applications connect to external tools and data sources. Without MCP, AI developers face the same fragmentation problem that web developers faced before standardized APIs—they must create custom connections for each external system their AI needs to access.

What is MCP?

Model Context Protocol (MCP) is an open protocol developed by Anthropic that enables seamless integration between AI applications/agents and various tools and data sources. Think of it as a universal translator that allows AI systems to communicate with different external tools without needing custom code for each connection.

Two weeks ago something huge happened in tech! Cloudflare, cloud platform that offers DNS and DDoS protections service, auto mitigated a 3.8 Tbps DDoS attack. To put that in perspective, imagine downloading 950 HD movies... every single second. That's the kind of digital tsunami Cloudflare was up against.  Let’s demystify what goes into mitigating an attack of this magnitude. Before we understand that, let me start by sharing how DDoS attacks work.