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Join us in the first episode of Azure AI Series on September 7, 10:00 AM EDT with Stephen SIMON.
ABSTRACT
Azure AI provides proven secure, and responsible AI capabilities on the cloud. You can build mission-critical solutions that can analyze images, comprehend speech, make predictions using data, and imitate other intelligent human behaviors—all using Azure AI.
AGENDA
Tune in to know the entire ecosystem of Azure AI and the different services provided by Microsoft. In this live show, we'll go through the following topics.
- Azure Cognitive Services
- Machine Learning
- Azure Bot Service
- Azure Databricks
- Azure Cognitive Search
We'll also see how you can get 200 USD free credit in your Azure account to get started.
C# Corner - Global Community for Software and Data Developers
https://www.c-sharpcorner.com
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0:30
Thank you
1:30
Thank you
1:59
Thank you
2:29
Hi, everyone. Welcome back to C Sharp Corner Live Show. I'm your host, Stephen Simon, and
2:48
I'm your only host for today. I mean, for today's session and for this AI show, I'm going to be
2:56
your host and also I'm going to be your guest. So if you are joining us for the very first time
3:02
welcome to C Sharp Corner live show. We do these live shows quite often in a week. If I talk very
3:09
specifically about this Azure AI show, this will be streamed every Monday at 7.30 p.m. Indian time
3:17
zone and 10 a.m. Eastern time zone. And if you are someone who are joining us from Europe
3:22
It is 4 p.m. Central Eastern Summer Time Zone. So those are the time zones and that's exactly on Monday every day
3:32
So let us know in the comment section below that from which location you are joining so that I can come to know on where this audience is actually reaching
3:40
So before I go ahead and get started once again, hi, everyone. Welcome to the live show
3:45
My name is Stephen Simon and this live show that is Azure AI show is dedicated for every single
3:52
individual out there who wants to move into the field of Azure AI and machine learning
3:58
We're going to cover the entire ecosystem of Azure when it comes to the services that Azure has to
4:04
provide in the field of AI and machine learning. In the very first episode of this Azure AI show
4:10
So we're going to talk about some basics of what is Azure, what is AI, what is machine
4:16
learning and how you can actually leverage the possibilities of AI and machine learning
4:23
in the cloud platform that is Azure. So Arvait, hi, Swami says he's joining us from London
4:32
Hosim, hi, welcome to the live show. You're joining us from Egypt
4:36
So it looks like we have quite an international audience, global audience
4:40
So even before, since I believe some of you may be joining us for the first time, let me go ahead and pull up my screen
4:49
Okay, perfect. And let me introduce you where I actually add all these live shows
4:55
So if you go to platform that is csharpcorner.com, which is actually a global community of software and data developers
5:02
And in here, you will find a section that says upcoming events
5:05
So as you can see, it's September 7th. It says get to know Azure AI MLN ecosystem
5:12
And if you click on upcoming events, you will find all the events happening over here
5:16
So that is something if you want to know about what is the next events happening and what
5:21
is the next Azure AI event happening and what am I actually going to cover
5:25
You can visit this platform, csharpgarner.com slash chapters, and you will find all the details
5:31
over here. So thank you, Sundar. thank you for joining us from kerala thank you so much uh happy birthday that really means a lot
5:39
and raghav you're joining us from chennai thank you thank you so much for everyone who has joined
5:45
us today thank you deep up for joining us so let us go ahead and talk a little bit about uh
5:52
data data science how it has influenced our day-to-day lives and then we're actually going
5:57
to move into and understand the azure ai and different services that microsoft has to provide
6:03
And remember, this show is only for 30 minutes, right? It means, I mean, we have already passed six minutes
6:09
and now we're left with only 25 minutes, and I'm going to cover as soon as possible
6:13
so that your time is most valuable, right? So the very first thing is that data is everywhere, right
6:20
If you just look at the data we are producing every day is humongous
6:26
In a record, it says that Google proposes almost 24 terabytes of data every day
6:33
right but facebook has more than 10 millions of photos uploaded every hour youtube has over one
6:39
hour of video uploaded every second there are 400 million tweets done per day and it said that the
6:45
the satellites produce over hundreds of petabytes and by the year 2020 end the digital universe
6:52
would have made approximately 44 zettabytes of data so you all can actually imagine the amount
6:59
of data that we are generating and the the insights that we can actually grab from all this data and
7:06
that is where this data science machinery comes into play where you can actually use this data
7:12
get insights and actually build a better solution so uh okay now when it comes to data we do know
7:21
that we do produce the data even as an individual a lot on a day-to-day basis we upload pictures on
7:26
Facebook, Twitter, we go and search on Google. There are different kinds of data that are available
7:32
and they're available in different sizes and flavors. It can be a text data that we do on
7:37
Google, right? It can be numbers, of course the Excel sheets. It can be click streams. Now click
7:43
streams are the one that you go to google.com and what are the results that you get? You click on it
7:48
so somewhere it is stored in your history, right? Now the next two types of data that is graphs and
7:56
tables that is very similar right you get to see the data stored in a table form very often
8:02
and if a data is in the form of table you can actually put it in a graph and if the data is
8:07
in the form of graph you can actually put it in a table so both are very much relatable
8:12
so the next comes is the images right and they talk about the amount of images that we create
8:18
but something that is very interesting to note here that even images are kind of the tables
8:23
or i should say a matrix as you can see image is a combination of zeros and one and that is actually
8:29
stored in the form of matrix and if it is a multi-color image it is stored in a multi-layer
8:34
matrix which of course comes into the part of how you can actually process it to image recognition
8:40
something that we are definitely going to cover in the context of azure ai and machine learning
8:44
and we will look into the upcoming series now of course we do we create a lot of videos content and all that so that is the type of data we generate and so much data generated that we really need to go ahead and process it
8:58
But even before we go ahead and even start talking about the Azure AI and ML ecosystem
9:04
I believe we have to actually break the terms that what is Azure, what is AI and what is actually
9:11
machine learning and if we understand all these three terms individually it's really going to help
9:16
us to understand onto what azure has to provide when it comes to ai and machine learning so let's
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go ahead and first talk about the machine learning a very very simple exact definition a very layman's
9:30
that's definition that you can find on the internet is when machine can learn itself it's called
9:35
machine learning very simple uh nothing very complex to understand very simple right if machines
9:41
can go in and start learning on their own it is called machine learning uh but this this definition
9:47
is kind of not complete because how do they actually learn it right that is the question
9:52
if you can let us mean in the comment section below that how do actually machines learn that
9:57
would be great now let me add one more factor is how do actually machines learn that is actually
10:02
using the past experience, right? So now when machines can learn from their past experiences
10:10
it is called machine learning. Now our definition is getting a little better, right? It's making a
10:15
little more sense that machines can learn from past experience, then it is called machine learning
10:21
Now this past experience over here actually means the data that you provide, right? The data can be
10:27
in the form of images, voice, text, Excel sheets, and all that. So when you give the data to a machine, some pre-existing data, right
10:34
and it uses it to actually learn, it is called machine learning
10:38
But hey, our definition, it's still not complete. We have one more factor to add, and that is the better output
10:47
And only that is when our definition is completed. And this is how our definition looks like
10:53
When machine can learn from its past experience to give a better output
10:57
then it is called machine learning. And the reason is you just don't want to get an output
11:02
from a machine learning perspective just to get an output right. I mean, I as a human can definitely give outputs
11:10
I have a brain, I can store the data and I can process it and I can definitely give a lot better
11:16
The only reason that we use machine learning as a concept to go ahead and train our models
11:22
is because we as a human have a capacity to how much data we can store
11:26
and how much we can process at a single time. Whereas when it comes to machine
11:31
they have actually, I can say, kind of unlimited data, right? And the compute power is humongous
11:39
which is, of course, a lot powered by the cloud these days. So when the definition of machine learning now actually becomes
11:47
that's when machine can learn from its past experience to give a better output, it is called machine learning
11:54
So I think if you are joining us for the very first time, so this is the very Lee Mann's
11:59
definition that I can provide you for the machine learning. And welcome back once again, who are joining us for the first time
12:06
Welcome, Mark. Thank you for joining us from USA. Thank you for your time
12:11
Thank you, Shivangi, for joining us from India. Now let us go ahead and take one practical example and try to understand this machine
12:20
learning with an example, right? So in a traditional computer, what you actually do is you give your system a program and you give it data, right
12:28
That is actually your input, right? And then you actually tell what you need to perform on it
12:34
You give it to the computer, the computer processes it, and it gives you an output, right
12:39
This is a very simple way that we did study in our maybe primary school, right
12:44
Give your computer input, it processes it, and it gives the output. This is how your traditional computer programs
12:50
To understand it a little more better, let us go ahead and take an example
12:57
Now, you have given your computers an input of 2 and 3, and you are telling that you want to perform an addition operation on it
13:06
So you give 2 and 3 the addition operation, you give it to the computer, it processes it, and it gives you an output exactly on how your computer performs
13:15
right? But when it comes to the machine learning, the process kind of changes, right? This time
13:23
what you're doing is you're giving the data. At the same time, you're giving an output to the
13:27
computer. And towards the end, what you expect is a program. Now, to understand this, let me go ahead
13:35
and again, let's look at the example. That is, this time in the machine learning approach
13:41
what we are doing is we are giving the input as 2 and 3 and the other input as 5. And what we
13:47
actually want machine to figure out is how the value 5 is evaluated when we give the input as 2
13:55
and 3. Or I can even say on what is the relation between 2 and 3 when it comes to 5. So when I give
14:02
this to the computer, it figures it out and tells that the output, the relation between the 2 and 3
14:09
and 5 is the addition function by addition operation. Now the major benefit of this is that
14:16
now I have a kind of function right now I do know what is the relation between an input and an output
14:22
so the next time when I get an input I can actually go ahead and perform that operation and get an
14:28
output. Now this is to give a very simple example that this is your linear regression that is
14:33
y is equal to mx plus c. If you are able to calculate y and mx, m and x, you can definitely
14:42
calculate y. Something that we're definitely going to cover in the future videos. But this is how I
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will say you actually go ahead and define and understand machine learning. We are almost halfway
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down to our show and I'm going to now move to what is AI. I do not have actually slides for the AI
14:58
but you must think of artificial intelligence as a superset of machine learning and deep learning
15:05
artificial intelligence is just one way to bring uh the intelligence into your existing applications
15:12
and the systems that you run anything that helps your system or an application to take decisions
15:20
on its own you need not to have a human interaction that is how you will say it is the artificial
15:26
intelligence. Whereas if I talk about the machine learning, machine learning is actually a subset of
15:32
artificial intelligence. And when it comes to deep learning, deep learning is again a subset of
15:37
machine learning. So I can say that if you are doing machine learning, then you are definitely
15:43
doing artificial intelligence. But if you are working in artificial intelligence, maybe you're
15:49
working on machine learning but maybe not right so that was about the AI and
15:56
machine learning but here things gets really interesting that now what does
16:01
as you has a role to play right we did talk about the AI we did talk about the
16:06
machine learning what does as you has any role to play all right so let me
16:13
prove my system okay perfect so when you think of measure right the benefit of
16:19
Azure is amazing. All this training, testing and working of the data that you do, you do it in your
16:26
local system, right? And sometimes doing this gets really, really challenging. So what you actually do
16:32
that Azure is a cloud computing platform that is powered by Microsoft. So this is a platform
16:38
for cloud computing that is by Microsoft. And this has grown humongous and exponentially in the past
16:44
decade and people have been building some really amazing cloud applications using Microsoft Azure
16:51
Now this live Azure AI show is dedicated only for the Azure purpose whereas there are many other
16:58
cloud services like Google Cloud, Alibaba and also Amazon and IBM Watch Sunday also provides
17:04
some really good services. So if you're watching this show we do it gonna do it every Monday at
17:10
7 30 p.m indian time zone and 10 a.m monday eastern time zone so tune in and we're going to
17:17
explore the entire ecosystem of ai and ml that microsoft azure has to provide uh one of the key
17:24
benefit of the azure or any of the cloud computing is that it it is global yet it is local right
17:31
so what you see on your screen right now is the data centers that microsoft azure provides now if If you are in the USA you can actually choose where to store your data it can be in the south central US it can be in the eastern coast it can be in the western coast if you are joining us
17:49
from Europe you have a bunch of data centers available over there Asia China Australia
17:57
and almost in the entire globe and we also have the one availability region in the
18:04
Latin America. So, although Azure is, your data is stored at a global place, but it helps you
18:13
actually localize your data and store your data in very nearest possible location to your audience
18:20
I mean, if you are, if your audience is from the United States, you don't want to store your data
18:25
in the Europe or in the Asia, right? Now, if you think your audience is from Europe, you can
18:32
Now, of course, choose any of the data centers that are available and you can go ahead and put your data over that
18:37
So those are the benefits of the Azure, right? So now let us go ahead and see what Azure, what are the different services that Azure has to provide when it comes to the Azure AI and machine learning functionalities
18:52
So I'm going to do a very rough Google search, right? I'm going to go to the incognito mode, right
18:58
And if you're joining, if you're still here and watching the show, let me know in the comment section below to what extent of machine learning and AI you have used or what are the applications that you feel in your day to day life that is affected by AI and machine learning
19:14
Let me know in the comment section below and it will be interesting to see on what are your thoughts
19:19
So let us quickly go ahead and just search for Azure, right
19:23
And we're going to search for Azure. That's it. And I'm going to click on Microsoft Azure
19:28
Microsoft dot Azure dot Microsoft dot com. Oops. Okay. Don't I have it? Oh, my bad. Okay
19:35
Yes, I think I need to search here. Okay. I'm going to search for Azure, right? And the very
19:39
first thing that I get is the Microsoft Azure Cloud Computing Services. I'm going to click on
19:43
it and this is going to take me to the homepage of Azure, right? Now what I'm going to do is I'm
19:48
going to go to the products at the top and you see there are tons of products. I mean, there are
19:53
than 200 products that Microsoft Azure has to provide on the fly. But we are concerned only
19:59
with this Azure AI and machine learning. Now, if I go ahead and click on that, broadly speaking
20:05
in the category of AI and machine learning, Azure has five services that are very popular
20:12
The first one is cognitive services, bot services, machine learning, Databricks, AI Power Cloud Services, mobile and web apps. So let us quickly go ahead and do a peekaboo
20:23
look on what are the different services, what do they do, and how you can go ahead and get started
20:29
that we will see in the next video, right? Next slide show. Okay. The very first services that we
20:36
have is the Azure Cognitive Service. Now, this is a very amazing services that Microsoft has brought
20:42
for everyone who comes from a development background. Now, we do know that not everyone
20:47
can become a data scientist, go ahead and start building machine learning models
20:51
training, look at the accuracy, check out the prediction, if they are well or not, and
20:56
then eventually deploy. That is not something everyone can go ahead and do it
21:00
And also the problem is that it really takes a lot of your time. So what Azure has done is it has provided this Azure Cognitive Services, which gives
21:08
you a very handful of REST APIs that you can use it and just call the APIs and you will
21:14
get the output. Now there are a bunch of services that this cognitive services has to apply
21:22
Provide that you can do anomaly detection, you can do content moderator, you can do some
21:28
language processing, but if you want to do a text reading, you can definitely do that
21:33
You can do text ysis, you can do a Q&A maker, you can create a translator
21:38
In the same way, you have the speech services available. You can do speech to text, you can do text to speech, speech translation
21:46
Then you have this vision APIs available. You can do face recognition, you can do computer vision
21:52
ink recognition and web search, Bing search. So these are the all services that Microsoft provides you on the fly
22:00
You need not do anything. You just need to go ahead and create a service in the Azure
22:04
that we are definitely going to look in the upcoming live shows. Get your API credentials over there
22:11
if you have an existing application you can you just need to do is on you need to create an api
22:17
call and it will give you a response in the json and you can eventually go and start using uh adding
22:24
the ai capabilities in your application that is how simple it is to go ahead and get started
22:29
so if you don't have a data science background yet you want to use the ai services and want to save
22:36
your time uh this is azure cognitive services is something you would definitely like to go ahead
22:42
and check it out uh the next services that we have is the azure bot services now this is pretty uh
22:50
really cool uh services that that microsoft has provided is you can actually go ahead and create
22:56
your azure bot on the fly now now in the bot there are many many controls that you can do you can
23:02
create a Q&A bot, right? If you have your data already on your cloud or in your premises
23:10
you can use this Azure Bot Services to create a conversational AI. You can definitely convert
23:15
your e-commerce application into bring the more like a human interaction, adding a chat bot over
23:22
there. You need not go ahead and create the entire ecosystem of a chat application. You can use this
23:28
Azure Bot Service to go ahead and get started. Now, as I said in the previous discussion
23:34
Azure Cognitive Services provides tons of APIs. Even this, you can use those APIs and the AI capabilities from that
23:42
and add speech, search, language understanding, Q&A maker, and this vision services in your chatbot
23:51
which gives you a very handy approach to add these capabilities in your chatbot
23:57
You need not go ahead and get started from very scratch. And integrating these bot services in your existing application
24:06
it may be a mobile app, it may be a web application, it's very easy
24:10
And we're going to definitely look in the coming live shows on how you can go ahead and get started
24:16
And one of the very interesting thing is that now you may definitely want to go ahead and put it
24:21
if you're doing an e-commerce website, you definitely want to go ahead and put your chat applications
24:26
in your website or a mobile app. But this Azure Bot Services provides you a different channel
24:33
just like click and go to host your Bot Services. Either you can host your Bot in Microsoft Teams
24:40
Telegrams, Skype, Messenger, Line, and Cortana, right? I have used Skype and Cortana
24:49
It's pretty handy, right? Since it's Microsoft products, it gives you all services on the fly
24:53
So that is one other service you want to go ahead and definitely want to check it out
24:58
And we're going to look it in the next coming videos or the live shows
25:03
All right. So let's go ahead and figure out the next services that Michael has to provide
25:08
That is the machine learning. Quite a humongous services. It has an end-to-end machine learning lifecycle and it has a lot of things to provide
25:19
You can go ahead and start building your machine models. You can train, you can check the accuracy, you can deploy your machine learning models
25:28
over here. You can also have some REST APIs created for it so that once you have created your custom
25:35
machine learning models, you can just go ahead and start consuming them using the REST APIs
25:42
Now, when it comes to what are the different services that this particular product has to
25:47
provide, one of the things that have been in existence in the Azure ecosystem is that
25:52
for a very long time is this low code, no code approach
25:56
that is also called Azure Designer now, which was already called Azure Machine Learning Studio
26:02
So if you see, if you have any background in the machine learning
26:06
you would see that there's certain steps involved in machine learning that is quite repetitive
26:10
right, just like adding your libraries, then importing your data, splitting your data
26:17
visualizing your data, then passing, doing with some of the algorithms. So these are the things that are quite repetitive and as a developer you definitely want to go ahead and save your time So this drag and drop which is a low code no code approach you can actually go ahead and start building your machine learning experiment over here And there some really cool features like you can have your machine learning deployed
26:42
You can train your model. And there are also some inbuilt data sets available so that you can actually test it
26:48
So if you want to save your time, you can definitely use this approach
26:52
Many developers love it. And in the next episode, we are going to look at this and the other product
26:58
Now, from the other developers perspective, the other one that is quite famous is the
27:03
MLOps, which is actually the DevOps for machine learning. It provides you the streamlined machine learning lifecycle for building models to deploy and
27:13
manage your machine learning models. You can manage the production of workflows at scale using advanced alerts and machine
27:20
learning automation capabilities. You can of course profile validate, deploy machine learning models anywhere from cloud
27:27
to the edge to manage production and will work at scale in an enterprise-ready fashion so something
27:34
that that might be used uh often these days because people really need this devops feature
27:41
really helps you uh put put your production and the deployment and the development things very
27:47
easy for your entire team so this is something also that we are going to cover the the other
27:53
Another thing that of course we have a responsible ML. I'm going to leave it for this one now, but this is pretty interesting
27:59
I'm going to look at it later on by something beyond the scope for the very first video
28:05
but quite interesting. Now, when it comes to this Azure machine learning platform that we have, it provides you and
28:12
tons of tools, languages and frameworks that you can actually go ahead and start using them
28:18
A few of the tools that we have is the Visual Studio, Visual Studio Code, you can use the Jupyter Notebook, you can use PyCharm, it's quite famous in the field of data science
28:29
now if you have a preference of programming languages like Python or R
28:36
you can also use that and if you want to use frameworks of deep learning or
28:40
machine learning like scikit-learn tensorflow by torch keras right and chenner all these libraries and frameworks are available on the fly on
28:51
the ash or you can just go ahead and start using it so that that was one of
28:58
thing that this has provided Azure Machine Learning has to provide and one of the very cool
29:02
that thing is the automated machine learning that Azure has to provide that we're going to look in
29:07
the coming videos so if you have any questions let me know in the comment section below the very
29:12
first episode is quite a theoretical approach where we're just going to look to have an overview of
29:18
what are the different services uh the next thing that we have is the Azure Databricks now this is
29:25
for the guys who come from the Apache or the Spark background or maybe from the big data
29:32
Now, this is a service that allows you to use your big data, right, and use the data
29:38
that you have over there, put it in a machine learning ecosystem and use the, and find the
29:45
insights from there. So, of course, as you can see, it says that you can quickly start with an optimized Apache
29:52
Spark environment. So you can of course go ahead and create an environment and integrate with the existing services
29:59
That I sure has to provide on the fly when it comes to as your an AI
30:04
It definitely if you have if you come from a Python or our programming language
30:09
You need not go ahead and change your programming preferences. You can use your favorite
30:15
Programming tools and languages and you can of course do a version control with github or even with the Azure DevOps
30:23
definitely something enterprise would like to use it and the guys who come from the big data
30:30
This is also something that we are going to learn in the coming live shows
30:35
The last product that we have is the Azure Cognitive Search, which is an amazing product
30:41
It was earlier known as just the Azure Search, but now it is known as Azure Cognitive Search
30:47
because it comes with the various ai capabilities that bings have that microsoft bing product has
30:54
been using for a very long time to provide a better search engine for their platform now if
31:00
you own an e-commerce platform or you own a platform that needs a an advanced search engine
31:06
for your application you can use this azure cognitive search that will provide you some
31:12
really create capabilities it is very easy uh now if your data is on your on-premises it's totally
31:18
fine whereas if your data is on i assure even that it takes care of this all very nicely and one of
31:25
the very cool feature that this azure cognitive search provide is that you can create a searchable
31:30
content using integrated ai i mean if there's as you can see if there's a document that has a text
31:37
some images location names you can actually go ahead and extract the data from this uh this pdf
31:45
or a word file and actually go ahead and search it now of course behind the scenes we are using some
31:52
very powerful azure cognitives apis that microsoft provides uh to enhance the azure cognitive search
32:00
uh features that is why it is known as cognitives and and definitely uh it can uh it provides
32:06
advanced data security and compliance and it works with tons of data uh different kinds of
32:11
data you provide it can work on the images pdfs and all and definitely text and all different
32:16
searches so these are the top five uh services of azure ai and machine learning that it has to
32:24
provide but if you want to go ahead and look at all the different other services that azure has to
32:28
provide i think i'm almost uh two minutes over the lecture that i promised two minutes for the
32:34
countdown right two or three minutes okay uh now if you go to the azure.com azure.mexup.com
32:40
services and click on the ai and machine learning you can see there's tons and tons of services
32:47
available we covered only five of them today right we covered only five of them to understand whether
32:52
tons of services available over here that you can actually go ahead and and start using it right
32:58
I will try to cover some of the very important services like machine learning, cognitive search, Azure cognitive APIs and bot services, and maybe a few more from here like Azure open data set, which is platinum that provides you many data sets
33:13
And we're also going to look on how you can actually go ahead and start building
33:18
So to revise to what we have learned in this live show is that Azure has a lot to provide
33:28
when it comes to AI and machine learning. It doesn't have to be that you must be a pure data scientist or you must have any prerequisite
33:37
to move into Azure AI. Services like Azure Cognitive Services allows you to integrate the AI and machine learning
33:44
capabilities in your existing application and make your system intelligent. Now, the other thing that we have learned is that you can create bot services
33:53
If you are a hardcore programmer who likes to put in Python or R and like to use extensive
33:59
machine learning libraries like TensorFlow or Keras, you can definitely go ahead and
34:04
also use your existing skills on the Azure. In the next episode, we are going to look on how you can use automated machine learning
34:13
to upload your data into the cloud, right? And find out which is the best algorithm suited
34:20
for your particular machine learning, for your data sets, right? And if you have any questions to ask
34:28
you can go ahead and comment in this section, or you can always reach me out on my Twitter
34:35
that is twitter.com slash code with Simon, that I'm gonna put it in the comments, right
34:40
So if you're watching us, you can always find the latest comments from, uh, coming with the show from here
34:46
And definitely you can find the upcoming shows on csharp.com slash chapter
34:52
That was all from my side in this video. Thank you so much for joining us today
34:55
Uh, if you have reached the video till here, I'm going to catch you on next Monday, 10
35:01
AM Eastern time zone and 7 30 PM in Indian time zone
35:07
Uh, till then, uh, my name is Stephen Simon. I act as a traditional committee director at C-sharp corner and I'll catch you after seven days
35:17
and thank you so much for tuning in and have a good day
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