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Join this live session with Magnus Mårtensson ft. Samuel Gomez for the next episode of The Cloud Show with Magnus Mårtensson on July 10, at 01:05 PM (EST).
The show is about cloud leadership and all the important questions relating to cloud projects. Certainly, many matters when a company is going to and wants to be successful in the cloud, are about technology. However, there are many additional matters, adjacent to technology, that we also need to tend to regarding business strategy, human resources, organizational change, planning for a technical cloud approach, and many more questions. These conversations are critical for a healthy cloud and for a swift and accurate cloud approach.
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0:02
hello everyone and welcome back again to
0:05
hello everyone and welcome back again to
0:05
hello everyone and welcome back again to the cloud show so ai ai ai everywhere
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the cloud show so ai ai ai everywhere
0:09
the cloud show so ai ai ai everywhere everything has to be AI I got a
0:12
everything has to be AI I got a
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everything has to be AI I got a commercial for a from a phone company
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commercial for a from a phone company
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commercial for a from a phone company saying they now have are releasing AI
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saying they now have are releasing AI
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saying they now have are releasing AI versions of their phones like apparently
0:19
versions of their phones like apparently
0:19
versions of their phones like apparently everyone has to have ai everywhere Azure
0:22
everyone has to have ai everywhere Azure
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everyone has to have ai everywhere Azure has a lot of AI services so today on the
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has a lot of AI services so today on the
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has a lot of AI services so today on the cloud show we're going to talk about
0:27
cloud show we're going to talk about
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cloud show we're going to talk about which Azure Services there are and when
0:29
which Azure Services there are and when
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which Azure Services there are and when you use them and so forth and we're
0:31
you use them and so forth and we're
0:31
you use them and so forth and we're going to do that with an MVP uh an AI
0:35
going to do that with an MVP uh an AI
0:35
going to do that with an MVP uh an AI expert and we're going to talk today on
0:37
expert and we're going to talk today on
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expert and we're going to talk today on the cloud show with Sam
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the cloud show with Sam
0:39
the cloud show with Sam [Music]
0:49
Gomez hello there Sam hey Magnus how's
0:53
Gomez hello there Sam hey Magnus how's
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Gomez hello there Sam hey Magnus how's it going going very well it's pleasure
0:55
it going going very well it's pleasure
0:55
it going going very well it's pleasure to have you on the show thanks for
0:57
to have you on the show thanks for
0:57
to have you on the show thanks for having me I appreciate it absolutely are
1:00
having me I appreciate it absolutely are
1:00
having me I appreciate it absolutely are you an AI
1:01
you an AI
1:01
you an AI expert uh I don't know you know I I I
1:04
expert uh I don't know you know I I I
1:04
expert uh I don't know you know I I I rather have other people say if I am an
1:07
rather have other people say if I am an
1:07
rather have other people say if I am an expert or not I definitely I'm
1:09
expert or not I definitely I'm
1:09
expert or not I definitely I'm passionate about it for sure great
1:12
passionate about it for sure great
1:12
passionate about it for sure great that's wonderful well that's good
1:13
that's wonderful well that's good
1:13
that's wonderful well that's good because I I know that there are a lot of
1:15
because I I know that there are a lot of
1:15
because I I know that there are a lot of listeners out there who want to know
1:17
listeners out there who want to know
1:17
listeners out there who want to know about Ai and AI services and when and
1:19
about Ai and AI services and when and
1:19
about Ai and AI services and when and how to use which and what and so forth
1:21
how to use which and what and so forth
1:21
how to use which and what and so forth but before we get into all of that how
1:23
but before we get into all of that how
1:23
but before we get into all of that how about you tell us quickly or briefly who
1:25
about you tell us quickly or briefly who
1:26
about you tell us quickly or briefly who you are Sam sure um so right now I'm my
1:29
you are Sam sure um so right now I'm my
1:29
you are Sam sure um so right now I'm my client partner at a software consulting
1:31
client partner at a software consulting
1:31
client partner at a software consulting company called jica so we specialize in
1:34
company called jica so we specialize in
1:34
company called jica so we specialize in custom software development mostly on
1:36
custom software development mostly on
1:36
custom software development mostly on the Microsoft and Azure environments uh
1:40
the Microsoft and Azure environments uh
1:40
the Microsoft and Azure environments uh been doing this uh software development
1:42
been doing this uh software development
1:42
been doing this uh software development thing for 17 years uh and I just you
1:44
thing for 17 years uh and I just you
1:44
thing for 17 years uh and I just you know love that problem solving part of
1:47
know love that problem solving part of
1:47
know love that problem solving part of the work you know the aha moment when
1:49
the work you know the aha moment when
1:49
the work you know the aha moment when you get solve something that nobody has
1:51
you get solve something that nobody has
1:51
you get solve something that nobody has solved before or something that's been
1:53
solved before or something that's been
1:53
solved before or something that's been bothering you for a while so um very
1:56
bothering you for a while so um very
1:56
bothering you for a while so um very passionate about AI in recent years
1:58
passionate about AI in recent years
1:58
passionate about AI in recent years obviously um for the potential and
2:01
obviously um for the potential and
2:01
obviously um for the potential and everything that that you can do with it
2:03
everything that that you can do with it
2:03
everything that that you can do with it so apart from technology I'm a big
2:06
so apart from technology I'm a big
2:06
so apart from technology I'm a big soccer fan or football fan depending on
2:08
soccer fan or football fan depending on
2:08
soccer fan or football fan depending on where you are um so uh where are you by
2:12
where you are um so uh where are you by
2:12
where you are um so uh where are you by the way I'm sorry you're not in Europe
2:15
the way I'm sorry you're not in Europe
2:15
the way I'm sorry you're not in Europe then correct well I'm I'm in um I'm in
2:18
then correct well I'm I'm in um I'm in
2:18
then correct well I'm I'm in um I'm in the United States so I I grew up in
2:20
the United States so I I grew up in
2:20
the United States so I I grew up in Mexico so I've been I grew up calling it
2:22
Mexico so I've been I grew up calling it
2:22
Mexico so I've been I grew up calling it football but uh now if I say football
2:25
football but uh now if I say football
2:25
football but uh now if I say football over here in the US most of the time
2:27
over here in the US most of the time
2:27
over here in the US most of the time people start talking to me about
2:29
people start talking to me about
2:29
people start talking to me about American football
2:30
American football
2:30
American football which I know just the basics of so
2:33
which I know just the basics of so
2:33
which I know just the basics of so that's why you know depending where I'm
2:35
that's why you know depending where I'm
2:35
that's why you know depending where I'm at it's either soccer or football yep
2:38
at it's either soccer or football yep
2:38
at it's either soccer or football yep yep totally totally all right well I'm
2:40
yep totally totally all right well I'm
2:40
yep totally totally all right well I'm from Malmo Sweden right and that's where
2:42
from Malmo Sweden right and that's where
2:42
from Malmo Sweden right and that's where slatan Ibrahimovic came from so you know
2:44
slatan Ibrahimovic came from so you know
2:44
slatan Ibrahimovic came from so you know that's pretty I'm cool by
2:48
that's pretty I'm cool by
2:48
that's pretty I'm cool by proxy nice all right so let's get into
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proxy nice all right so let's get into
2:51
proxy nice all right so let's get into this so there are plenty of AI services
2:54
this so there are plenty of AI services
2:54
this so there are plenty of AI services in the Azure platform let's focus on
2:56
in the Azure platform let's focus on
2:57
in the Azure platform let's focus on those and um what's interesting of
2:59
those and um what's interesting of
2:59
those and um what's interesting of course to lot of of companies out there
3:01
course to lot of of companies out there
3:01
course to lot of of companies out there is what it really takes what kind of
3:03
is what it really takes what kind of
3:03
is what it really takes what kind of expertise it really takes to maneuver
3:06
expertise it really takes to maneuver
3:06
expertise it really takes to maneuver these it's one thing that they are there
3:07
these it's one thing that they are there
3:08
these it's one thing that they are there but who's going to use them and and you
3:09
but who's going to use them and and you
3:09
but who's going to use them and and you know for what so take it away sure thing
3:13
know for what so take it away sure thing
3:13
know for what so take it away sure thing um and yeah obviously Chad GPT Asher OPI
3:17
um and yeah obviously Chad GPT Asher OPI
3:17
um and yeah obviously Chad GPT Asher OPI right now geni in general it's it's it
3:21
right now geni in general it's it's it
3:21
right now geni in general it's it's it it's exploded in the last couple years
3:23
it's exploded in the last couple years
3:23
it's exploded in the last couple years and has brought um even more uh interest
3:27
and has brought um even more uh interest
3:27
and has brought um even more uh interest in AI because AI has been around for a
3:29
in AI because AI has been around for a
3:29
in AI because AI has been around for a while cloud and the uh you know the the
3:33
while cloud and the uh you know the the
3:33
while cloud and the uh you know the the performance that we have with cloud and
3:34
performance that we have with cloud and
3:35
performance that we have with cloud and the resources that we have have made it
3:37
the resources that we have have made it
3:37
the resources that we have have made it more accessible um but I want to take
3:41
more accessible um but I want to take
3:41
more accessible um but I want to take that attention that J has brought into
3:44
that attention that J has brought into
3:44
that attention that J has brought into into this realm and just focus on some
3:46
into this realm and just focus on some
3:46
into this realm and just focus on some services that Asher has had for a really
3:48
services that Asher has had for a really
3:48
services that Asher has had for a really long time that are a little bit more
3:50
long time that are a little bit more
3:50
long time that are a little bit more targeted it might be a better fit for
3:53
targeted it might be a better fit for
3:53
targeted it might be a better fit for certain scenarios nice you know there
3:55
certain scenarios nice you know there
3:55
certain scenarios nice you know there might be some organizations out there
3:57
might be some organizations out there
3:57
might be some organizations out there saying well it's cool you know jni and
4:01
saying well it's cool you know jni and
4:01
saying well it's cool you know jni and these this co-pilots and chat Bots but
4:04
these this co-pilots and chat Bots but
4:04
these this co-pilots and chat Bots but it's not really something that I can see
4:07
it's not really something that I can see
4:07
it's not really something that I can see uh being Ed in my organization so I want
4:09
uh being Ed in my organization so I want
4:09
uh being Ed in my organization so I want us I want us to take a look at what are
4:11
us I want us to take a look at what are
4:11
us I want us to take a look at what are the other things that are out there that
4:13
the other things that are out there that
4:13
the other things that are out there that maybe somebody goes and oh you know what
4:16
maybe somebody goes and oh you know what
4:16
maybe somebody goes and oh you know what that sounds like something that I could
4:17
that sounds like something that I could
4:17
that sounds like something that I could use and it's you know a lot easier to
4:19
use and it's you know a lot easier to
4:19
use and it's you know a lot easier to use maybe easier to implement than a gen
4:23
use maybe easier to implement than a gen
4:23
use maybe easier to implement than a gen AI solution so itic approach I love it
4:27
AI solution so itic approach I love it
4:27
AI solution so itic approach I love it all right yeah so I got a little graph
4:29
all right yeah so I got a little graph
4:29
all right yeah so I got a little graph of for you and for everyone shows the
4:32
of for you and for everyone shows the
4:32
of for you and for everyone shows the different services that are available
4:34
different services that are available
4:34
different services that are available out there and like you said what kind of
4:37
out there and like you said what kind of
4:37
out there and like you said what kind of expertise do you need in order to use
4:39
expertise do you need in order to use
4:39
expertise do you need in order to use some of these so uh if we can show that
4:42
some of these so uh if we can show that
4:42
some of these so uh if we can show that real quick I can go through
4:46
real quick I can go through
4:46
real quick I can go through those let's
4:48
those let's
4:48
those let's see if that
4:50
see if that
4:50
see if that works oh here we go here we go all right
4:54
works oh here we go here we go all right
4:54
works oh here we go here we go all right SOI what do we have in this environment
4:57
SOI what do we have in this environment
4:57
SOI what do we have in this environment what does Asher provide for us
5:00
what does Asher provide for us
5:00
what does Asher provide for us for AI and ml uh needs the very bottom
5:04
for AI and ml uh needs the very bottom
5:04
for AI and ml uh needs the very bottom we have uh Asher machine learning uh
5:06
we have uh Asher machine learning uh
5:06
we have uh Asher machine learning uh it's a machine learning platform where
5:08
it's a machine learning platform where
5:08
it's a machine learning platform where you can train your own models you can um
5:12
you can train your own models you can um
5:12
you can train your own models you can um test them obviously you can deploy them
5:14
test them obviously you can deploy them
5:14
test them obviously you can deploy them to web end points or you can deploy into
5:17
to web end points or you can deploy into
5:17
to web end points or you can deploy into batch Ed points so really takes your
5:21
batch Ed points so really takes your
5:21
batch Ed points so really takes your data all the way through until you can
5:23
data all the way through until you can
5:23
data all the way through until you can generate a model that can be deployed or
5:26
generate a model that can be deployed or
5:26
generate a model that can be deployed or integrated with your applications m just
5:29
integrated with your applications m just
5:29
integrated with your applications m just used this the service not that long ago
5:32
used this the service not that long ago
5:32
used this the service not that long ago but as uh last month as a matter of fact
5:35
but as uh last month as a matter of fact
5:35
but as uh last month as a matter of fact in a project where we were working with
5:38
in a project where we were working with
5:38
in a project where we were working with a museum to control the temperature and
5:41
a museum to control the temperature and
5:41
a museum to control the temperature and humidity uh conditions in different
5:44
humidity uh conditions in different
5:44
humidity uh conditions in different areas of the museum to make sure that uh
5:46
areas of the museum to make sure that uh
5:46
areas of the museum to make sure that uh the people there were comfortable but
5:48
the people there were comfortable but
5:48
the people there were comfortable but obviously protecting all the art that
5:50
obviously protecting all the art that
5:50
obviously protecting all the art that they have in the museum it's a very
5:52
they have in the museum it's a very
5:52
they have in the museum it's a very specific scenario and there's definitely
5:54
specific scenario and there's definitely
5:54
specific scenario and there's definitely not something that you can just say hey
5:57
not something that you can just say hey
5:57
not something that you can just say hey co-pilot control the temperature right
5:59
co-pilot control the temperature right
5:59
co-pilot control the temperature right that's not something that you're going
6:00
that's not something that you're going
6:00
that's not something that you're going to do in this in this situation you need
6:02
to do in this in this situation you need
6:02
to do in this in this situation you need something more automated something
6:04
something more automated something
6:04
something more automated something behind the scenes so that's where aure
6:07
behind the scenes so that's where aure
6:07
behind the scenes so that's where aure Azure machine learning comes
6:09
Azure machine learning comes
6:09
Azure machine learning comes in this I mean if I if I may like
6:12
in this I mean if I if I may like
6:12
in this I mean if I if I may like position this you said like it's a it's
6:14
position this you said like it's a it's
6:14
position this you said like it's a it's a fundamental thing u i I would say that
6:17
a fundamental thing u i I would say that
6:17
a fundamental thing u i I would say that this is this is a lot um data sciency
6:19
this is this is a lot um data sciency
6:19
this is this is a lot um data sciency it's very lowlevel it's not it's not one
6:22
it's very lowlevel it's not it's not one
6:22
it's very lowlevel it's not it's not one of those click easy to use Services you
6:24
of those click easy to use Services you
6:24
of those click easy to use Services you have to like be a data scientist here
6:27
have to like be a data scientist here
6:27
have to like be a data scientist here correct the interface is actually really
6:29
correct the interface is actually really
6:29
correct the interface is actually really easy to use however like you said you
6:31
easy to use however like you said you
6:31
easy to use however like you said you need to have the basic concepts of
6:33
need to have the basic concepts of
6:33
need to have the basic concepts of machine learning you know to you need to
6:35
machine learning you know to you need to
6:35
machine learning you know to you need to know at least the very basics of what is
6:38
know at least the very basics of what is
6:38
know at least the very basics of what is a regression problem what is a
6:40
a regression problem what is a
6:40
a regression problem what is a classification problem how your features
6:43
classification problem how your features
6:43
classification problem how your features affect your results what kind of uh what
6:46
affect your results what kind of uh what
6:46
affect your results what kind of uh what kind of things do you need to tweak in
6:47
kind of things do you need to tweak in
6:47
kind of things do you need to tweak in order to get the results that you're
6:49
order to get the results that you're
6:49
order to get the results that you're looking for so there's definitely
6:51
looking for so there's definitely
6:51
looking for so there's definitely something a little bit more involved and
6:52
something a little bit more involved and
6:52
something a little bit more involved and that's going to require a little bit
6:54
that's going to require a little bit
6:54
that's going to require a little bit more uh knowledge on the data science
6:57
more uh knowledge on the data science
6:57
more uh knowledge on the data science obviously you need to have some sort of
6:59
obviously you need to have some sort of
6:59
obviously you need to have some sort of data analysis background as well so that
7:01
data analysis background as well so that
7:01
data analysis background as well so that you can prepare your data so you can
7:03
you can prepare your data so you can
7:03
you can prepare your data so you can make sure and determine if your data is
7:06
make sure and determine if your data is
7:06
make sure and determine if your data is good enough to even train your model
7:09
good enough to even train your model
7:09
good enough to even train your model because that might be your stopping
7:11
because that might be your stopping
7:11
because that might be your stopping point that you don't have good data to
7:12
point that you don't have good data to
7:13
point that you don't have good data to begin with um but yes it's a lot more a
7:15
begin with um but yes it's a lot more a
7:15
begin with um but yes it's a lot more a lot more involved
7:17
lot more involved
7:17
lot more involved yep next we have um the API Services
7:22
yep next we have um the API Services
7:22
yep next we have um the API Services Suite where we have apis that have been
7:25
Suite where we have apis that have been
7:25
Suite where we have apis that have been some of them have been around for a long
7:27
some of them have been around for a long
7:27
some of them have been around for a long time and these are more common scenarios
7:30
time and these are more common scenarios
7:30
time and these are more common scenarios uh or you know problems so analyzing
7:33
uh or you know problems so analyzing
7:33
uh or you know problems so analyzing images it's a problem that has been
7:35
images it's a problem that has been
7:35
images it's a problem that has been around for a while it's a very common
7:37
around for a while it's a very common
7:37
around for a while it's a very common problem and Asher has an API for that uh
7:41
problem and Asher has an API for that uh
7:41
problem and Asher has an API for that uh Speech so text to speech speech to text
7:43
Speech so text to speech speech to text
7:43
Speech so text to speech speech to text again a very common problem uh language
7:46
again a very common problem uh language
7:46
again a very common problem uh language sentiment analysis uh extraction of uh
7:50
sentiment analysis uh extraction of uh
7:50
sentiment analysis uh extraction of uh phrases
7:51
phrases
7:51
phrases information and more recently we also
7:54
information and more recently we also
7:54
information and more recently we also have content safety so if you're trying
7:56
have content safety so if you're trying
7:56
have content safety so if you're trying to protect your users if you're trying
7:58
to protect your users if you're trying
7:58
to protect your users if you're trying to reduce the amount uh or you're trying
8:01
to reduce the amount uh or you're trying
8:01
to reduce the amount uh or you're trying to make a put a check on the information
8:04
to make a put a check on the information
8:04
to make a put a check on the information that you accept or the images that you
8:06
that you accept or the images that you
8:06
that you accept or the images that you can you can upload within that decision
8:08
can you can upload within that decision
8:08
can you can upload within that decision group that you see in the image that's
8:10
group that you see in the image that's
8:10
group that you see in the image that's where the content safety API comes in
8:13
where the content safety API comes in
8:13
where the content safety API comes in right obviously H I don't know if you've
8:16
right obviously H I don't know if you've
8:16
right obviously H I don't know if you've heard of a a little API called Azure
8:19
heard of a a little API called Azure
8:19
heard of a a little API called Azure openi um that is within this group as
8:23
openi um that is within this group as
8:23
openi um that is within this group as well um but this is obviously much more
8:27
well um but this is obviously much more
8:27
well um but this is obviously much more much easier to to use uh as developer I
8:30
much easier to to use uh as developer I
8:30
much easier to to use uh as developer I think one thing that we've all done at
8:32
think one thing that we've all done at
8:33
think one thing that we've all done at one point or another in our career is
8:34
one point or another in our career is
8:34
one point or another in our career is integrate with an with an API so if
8:37
integrate with an with an API so if
8:37
integrate with an with an API so if you're if you know how to do that you
8:39
you're if you know how to do that you
8:39
you're if you know how to do that you can use these apis and you can say hey
8:42
can use these apis and you can say hey
8:42
can use these apis and you can say hey my application is using API my my
8:45
my application is using API my my
8:45
my application is using API my my organization is using API so would you
8:48
organization is using API so would you
8:48
organization is using API so would you say that this is just Microsoft
8:51
say that this is just Microsoft
8:51
say that this is just Microsoft providing um a a service abstraction
8:54
providing um a a service abstraction
8:54
providing um a a service abstraction layer uh and underneath the covers they
8:57
layer uh and underneath the covers they
8:57
layer uh and underneath the covers they are they have implemented a service with
8:59
are they have implemented a service with
8:59
are they have implemented a service with an API that uses things like machine
9:01
an API that uses things like machine
9:01
an API that uses things like machine learning underneath yes exactly that's
9:04
learning underneath yes exactly that's
9:04
learning underneath yes exactly that's that's that's exactly what it is it does
9:06
that's that's exactly what it is it does
9:06
that's that's exactly what it is it does give you a little bit of wiggle room
9:08
give you a little bit of wiggle room
9:08
give you a little bit of wiggle room some of these services are customizable
9:11
some of these services are customizable
9:11
some of these services are customizable so if you have for example you're trying
9:14
so if you have for example you're trying
9:14
so if you have for example you're trying to analyze certain images and you see
9:16
to analyze certain images and you see
9:16
to analyze certain images and you see that what's out of the box is not good
9:18
that what's out of the box is not good
9:18
that what's out of the box is not good enough for your case or you have a very
9:19
enough for your case or you have a very
9:19
enough for your case or you have a very specific set of images that you're
9:21
specific set of images that you're
9:21
specific set of images that you're trying to train your model on you can
9:24
trying to train your model on you can
9:24
trying to train your model on you can provide those images label your images
9:26
provide those images label your images
9:27
provide those images label your images and you can build a custom model but
9:30
and you can build a custom model but
9:30
and you can build a custom model but yeah but it's not as customizable as
9:33
yeah but it's not as customizable as
9:33
yeah but it's not as customizable as using Asher machine learning with aser
9:35
using Asher machine learning with aser
9:35
using Asher machine learning with aser machine learning you have all these
9:37
machine learning you have all these
9:37
machine learning you have all these levers that you can pull to control
9:40
levers that you can pull to control
9:40
levers that you can pull to control which uh you know algorithm you're using
9:42
which uh you know algorithm you're using
9:42
which uh you know algorithm you're using and some of the other things you can
9:44
and some of the other things you can
9:44
and some of the other things you can control these apis you're a little bit
9:47
control these apis you're a little bit
9:47
control these apis you're a little bit more limited with that control got it
9:49
more limited with that control got it
9:49
more limited with that control got it all
9:51
all
9:51
all right um next uh our scenario based API
9:55
right um next uh our scenario based API
9:55
right um next uh our scenario based API so these are a very specific uh it's a
9:58
so these are a very specific uh it's a
9:58
so these are a very specific uh it's a combination in a lot of cases of the
10:01
combination in a lot of cases of the
10:01
combination in a lot of cases of the services in the middle document
10:03
services in the middle document
10:03
services in the middle document intelligence for
10:04
intelligence for
10:04
intelligence for example uh uses uh vision and language
10:07
example uh uses uh vision and language
10:07
example uh uses uh vision and language to extract information from your
10:09
to extract information from your
10:09
to extract information from your documents uh so it has apis that allow
10:12
documents uh so it has apis that allow
10:12
documents uh so it has apis that allow you to extract information from uh
10:14
you to extract information from uh
10:14
you to extract information from uh receipts invoices and it's got a lot of
10:18
receipts invoices and it's got a lot of
10:19
receipts invoices and it's got a lot of uh uh a lot of relevance lately because
10:22
uh uh a lot of relevance lately because
10:22
uh uh a lot of relevance lately because it's a big part of a rack pattern so a
10:26
it's a big part of a rack pattern so a
10:26
it's a big part of a rack pattern so a lot of companies what they're doing is
10:27
lot of companies what they're doing is
10:28
lot of companies what they're doing is they're using document intell Ence to
10:30
they're using document intell Ence to
10:30
they're using document intell Ence to extract information from documents and
10:32
extract information from documents and
10:32
extract information from documents and then store them in an ashure AI search
10:34
then store them in an ashure AI search
10:35
then store them in an ashure AI search index for example and then you feed that
10:37
index for example and then you feed that
10:37
index for example and then you feed that into a um into a gen AI application so
10:41
into a um into a gen AI application so
10:41
into a um into a gen AI application so so okay so is this what you would use if
10:44
so okay so is this what you would use if
10:44
so okay so is this what you would use if you wanted to like talk to your
10:46
you wanted to like talk to your
10:46
you wanted to like talk to your documents or ask it questions yeah so
10:48
documents or ask it questions yeah so
10:48
documents or ask it questions yeah so that would be your first step so your
10:51
that would be your first step so your
10:51
that would be your first step so your document intelligence you feed your
10:52
document intelligence you feed your
10:52
document intelligence you feed your documents you extract information uh and
10:55
documents you extract information uh and
10:55
documents you extract information uh and then that would be your the foundation
10:57
then that would be your the foundation
10:57
then that would be your the foundation of that application that talks your
11:00
of that application that talks your
11:00
of that application that talks your documents or talks to your data nice so
11:03
documents or talks to your data nice so
11:03
documents or talks to your data nice so um like I said very a little bit more
11:04
um like I said very a little bit more
11:04
um like I said very a little bit more specific um but all of these Services
11:08
specific um but all of these Services
11:08
specific um but all of these Services you either have to be a developer or
11:10
you either have to be a developer or
11:10
you either have to be a developer or data scientist in order to be able to
11:12
data scientist in order to be able to
11:12
data scientist in order to be able to use them right for sure like we said
11:14
use them right for sure like we said
11:14
use them right for sure like we said machine learning little bit more on the
11:16
machine learning little bit more on the
11:16
machine learning little bit more on the data science side the apis a developer
11:20
data science side the apis a developer
11:20
data science side the apis a developer and data science Sciences typically here
11:23
and data science Sciences typically here
11:23
and data science Sciences typically here you would write code to talk to an API
11:26
you would write code to talk to an API
11:26
you would write code to talk to an API and you would not just pull something
11:29
and you would not just pull something
11:29
and you would not just pull something onto a a screen with a connector right
11:32
onto a a screen with a connector right
11:32
onto a a screen with a connector right you have to actually use the API
11:33
you have to actually use the API
11:33
you have to actually use the API yourself yeah that is correct so it's
11:36
yourself yeah that is correct so it's
11:36
yourself yeah that is correct so it's more going to be behind the scenes um
11:38
more going to be behind the scenes um
11:38
more going to be behind the scenes um and not something that um a business
11:42
and not something that um a business
11:42
and not something that um a business user might be able to do maybe a very
11:44
user might be able to do maybe a very
11:44
user might be able to do maybe a very teex happy business user but U more on
11:46
teex happy business user but U more on
11:46
teex happy business user but U more on the developer side got
11:50
the developer side got
11:50
the developer side got it um as we continue you know we have
11:53
it um as we continue you know we have
11:53
it um as we continue you know we have the Power Platform um services so
11:57
the Power Platform um services so
11:57
the Power Platform um services so powerbi power apps power automate uh
12:00
powerbi power apps power automate uh
12:00
powerbi power apps power automate uh co-pilot studio so this is where we get
12:02
co-pilot studio so this is where we get
12:02
co-pilot studio so this is where we get into what you were just mentioning where
12:04
into what you were just mentioning where
12:04
into what you were just mentioning where it's a little bit more graphic a little
12:06
it's a little bit more graphic a little
12:06
it's a little bit more graphic a little bit more you know citizen developers to
12:08
bit more you know citizen developers to
12:08
bit more you know citizen developers to be able to integrate with some of these
12:10
be able to integrate with some of these
12:10
be able to integrate with some of these Services um co-pilot Studio obviously if
12:13
Services um co-pilot Studio obviously if
12:13
Services um co-pilot Studio obviously if you want to build your own co-pilot
12:14
you want to build your own co-pilot
12:14
you want to build your own co-pilot bring your own data and train it on that
12:17
bring your own data and train it on that
12:17
bring your own data and train it on that uh and then at the very top we have 365
12:20
uh and then at the very top we have 365
12:20
uh and then at the very top we have 365 Dynamics partner Solutions so services
12:23
Dynamics partner Solutions so services
12:23
Dynamics partner Solutions so services that are already have some sort of AI in
12:25
that are already have some sort of AI in
12:26
that are already have some sort of AI in their uh co-pilot if you enable co-pilot
12:29
their uh co-pilot if you enable co-pilot
12:29
their uh co-pilot if you enable co-pilot in in in Outlook or some of your other
12:31
in in in Outlook or some of your other
12:31
in in in Outlook or some of your other services you are already using Ai and
12:34
services you are already using Ai and
12:34
services you are already using Ai and again this is more for business users
12:37
again this is more for business users
12:37
again this is more for business users than the um service I was gonna say at
12:40
than the um service I was gonna say at
12:40
than the um service I was gonna say at this level is more like using it rather
12:42
this level is more like using it rather
12:42
this level is more like using it rather than um rather than building it I or
12:47
than um rather than building it I or
12:47
than um rather than building it I or yeah unless you're doing something with
12:49
yeah unless you're doing something with
12:49
yeah unless you're doing something with copilot Studio where you know you're
12:51
copilot Studio where you know you're
12:51
copilot Studio where you know you're you're doing you're still building
12:53
you're doing you're still building
12:53
you're doing you're still building because you're you might be uh
12:55
because you're you might be uh
12:55
because you're you might be uh customizing or building a custom
12:57
customizing or building a custom
12:58
customizing or building a custom co-pilot for your specific needs okay
13:00
co-pilot for your specific needs okay
13:00
co-pilot for your specific needs okay yeah got it okay cool this is a this is
13:03
yeah got it okay cool this is a this is
13:03
yeah got it okay cool this is a this is a very very good rundown uh makes makes
13:05
a very very good rundown uh makes makes
13:05
a very very good rundown uh makes makes a perfect sense there are layers to the
13:07
a perfect sense there are layers to the
13:07
a perfect sense there are layers to the cake yeah exactly frosting on
13:10
cake yeah exactly frosting on
13:10
cake yeah exactly frosting on top
13:12
top
13:12
top yeah so yeah like I said you know this
13:14
yeah so yeah like I said you know this
13:14
yeah so yeah like I said you know this is where um I want to shine a light on
13:17
is where um I want to shine a light on
13:17
is where um I want to shine a light on these Services
13:19
these Services
13:19
these Services because an organization might say okay
13:22
because an organization might say okay
13:22
because an organization might say okay you know what I don't see a use for a
13:25
you know what I don't see a use for a
13:25
you know what I don't see a use for a chatbot or a gen application but you
13:28
chatbot or a gen application but you
13:28
chatbot or a gen application but you know what I want to automate the way
13:30
know what I want to automate the way
13:30
know what I want to automate the way that we ingest or we do our data entry
13:34
that we ingest or we do our data entry
13:34
that we ingest or we do our data entry we we ingest our documents so document
13:37
we we ingest our documents so document
13:37
we we ingest our documents so document intelligence that be perfect a call to
13:39
intelligence that be perfect a call to
13:39
intelligence that be perfect a call to an API very easy to use and you're able
13:43
an API very easy to use and you're able
13:43
an API very easy to use and you're able to um read your uh invoices receipts
13:47
to um read your uh invoices receipts
13:47
to um read your uh invoices receipts there's a a a a whole list of apis or
13:51
there's a a a a whole list of apis or
13:51
there's a a a a whole list of apis or models I should say that are available
13:53
models I should say that are available
13:53
models I should say that are available you can create your own models as well
13:55
you can create your own models as well
13:55
you can create your own models as well if your document is very specific it has
13:57
if your document is very specific it has
13:57
if your document is very specific it has a very specific format
13:59
a very specific format
13:59
a very specific format you can even customize it that way so
14:02
you can even customize it that way so
14:02
you can even customize it that way so okay like I said it's important for
14:04
okay like I said it's important for
14:04
okay like I said it's important for organizations out there I think to know
14:06
organizations out there I think to know
14:06
organizations out there I think to know what else is out there what else they
14:08
what else is out there what else they
14:08
what else is out there what else they can use oh yeah yeah for sure so now
14:10
can use oh yeah yeah for sure so now
14:10
can use oh yeah yeah for sure so now that now we like kind of know that there
14:12
that now we like kind of know that there
14:12
that now we like kind of know that there are so many services and like the
14:14
are so many services and like the
14:14
are so many services and like the different levels here I I appreciate uh
14:16
different levels here I I appreciate uh
14:16
different levels here I I appreciate uh you laying out how how you would need to
14:18
you laying out how how you would need to
14:18
you laying out how how you would need to be a developer to use the following and
14:20
be a developer to use the following and
14:20
be a developer to use the following and so forth and that that makes a lot of
14:22
so forth and that that makes a lot of
14:22
so forth and that that makes a lot of sense so before as a company before I'm
14:27
sense so before as a company before I'm
14:27
sense so before as a company before I'm evening or we are evening even
14:29
evening or we are evening even
14:29
evening or we are evening even considering using any of these uh
14:31
considering using any of these uh
14:31
considering using any of these uh Services what are some things that we
14:33
Services what are some things that we
14:33
Services what are some things that we need to uh you know qu ask questions we
14:35
need to uh you know qu ask questions we
14:35
need to uh you know qu ask questions we need to ask ourselves for our business
14:37
need to ask ourselves for our business
14:37
need to ask ourselves for our business before we go on an you know in an AI
14:40
before we go on an you know in an AI
14:40
before we go on an you know in an AI Journey or
14:41
Journey or
14:41
Journey or whatnot yeah so I I think it's very
14:43
whatnot yeah so I I think it's very
14:43
whatnot yeah so I I think it's very important that um you don't try to fit
14:46
important that um you don't try to fit
14:46
important that um you don't try to fit the technology into your processes so um
14:50
the technology into your processes so um
14:50
the technology into your processes so um we've all seen the demos of uh gen Ai
14:53
we've all seen the demos of uh gen Ai
14:53
we've all seen the demos of uh gen Ai and the chat B and and all and all that
14:56
and the chat B and and all and all that
14:56
and the chat B and and all and all that um and I think the wrong approach would
14:59
um and I think the wrong approach would
14:59
um and I think the wrong approach would be to say okay how can we integrate this
15:01
be to say okay how can we integrate this
15:01
be to say okay how can we integrate this into our processes I think instead of
15:04
into our processes I think instead of
15:04
into our processes I think instead of that we have to look at our processes
15:06
that we have to look at our processes
15:06
that we have to look at our processes and companies should look at what are
15:08
and companies should look at what are
15:08
and companies should look at what are the biggest pain points that their users
15:10
the biggest pain points that their users
15:10
the biggest pain points that their users have uh are they are there any data
15:14
have uh are they are there any data
15:14
have uh are they are there any data entry um processes for example that we
15:17
entry um processes for example that we
15:17
entry um processes for example that we have people spending half their day
15:20
have people spending half their day
15:20
have people spending half their day doing data entry or they're doing data
15:22
doing data entry or they're doing data
15:22
doing data entry or they're doing data entry when they could be doing more
15:25
entry when they could be doing more
15:25
entry when they could be doing more important things right a very common
15:27
important things right a very common
15:27
important things right a very common scenario in you know here in the US and
15:30
scenario in you know here in the US and
15:30
scenario in you know here in the US and Healthcare is that a lot of nurses
15:31
Healthcare is that a lot of nurses
15:32
Healthcare is that a lot of nurses doctors spend too much time entering
15:34
doctors spend too much time entering
15:34
doctors spend too much time entering data when you know they could use that
15:37
data when you know they could use that
15:37
data when you know they could use that obviously you know to see their their
15:38
obviously you know to see their their
15:38
obviously you know to see their their patients so I think that's the first
15:41
patients so I think that's the first
15:41
patients so I think that's the first approach go for the low hanging fruit
15:44
approach go for the low hanging fruit
15:44
approach go for the low hanging fruit what can we do to say that we are an AI
15:48
what can we do to say that we are an AI
15:48
what can we do to say that we are an AI or that we using AI in our organization
15:51
or that we using AI in our organization
15:51
or that we using AI in our organization without going for the Big Bang because
15:53
without going for the Big Bang because
15:53
without going for the Big Bang because another thing that you have to consider
15:55
another thing that you have to consider
15:55
another thing that you have to consider is your users right when Chach PT came
16:00
is your users right when Chach PT came
16:00
is your users right when Chach PT came out we started to see all those articles
16:02
out we started to see all those articles
16:02
out we started to see all those articles and and everyone in the news that you
16:04
and and everyone in the news that you
16:04
and and everyone in the news that you know developers are going to be obsolete
16:06
know developers are going to be obsolete
16:06
know developers are going to be obsolete or AI is coming for our jobs and for for
16:10
or AI is coming for our jobs and for for
16:10
or AI is coming for our jobs and for for us that you know we we're kind of in the
16:13
us that you know we we're kind of in the
16:13
us that you know we we're kind of in the middle of it yeah we know that it's not
16:16
middle of it yeah we know that it's not
16:16
middle of it yeah we know that it's not going to happen you know that quick or
16:19
going to happen you know that quick or
16:19
going to happen you know that quick or or happen at all because we started to
16:21
or happen at all because we started to
16:22
or happen at all because we started to see Chad GPT making up libraries and the
16:24
see Chad GPT making up libraries and the
16:24
see Chad GPT making up libraries and the code is not performant and all these
16:27
code is not performant and all these
16:27
code is not performant and all these kind of things that we know that you
16:28
kind of things that we know that you
16:28
kind of things that we know that you know technology is not there yet but no
16:32
know technology is not there yet but no
16:32
know technology is not there yet but no yeah sorry go
16:35
ahead no no no no that's fine I I agree
16:38
ahead no no no no that's fine I I agree
16:38
ahead no no no no that's fine I I agree um it's not going to be that quick no
16:40
um it's not going to be that quick no
16:40
um it's not going to be that quick no yeah but for for people that are not in
16:43
yeah but for for people that are not in
16:43
yeah but for for people that are not in technology and that're not familiar with
16:44
technology and that're not familiar with
16:45
technology and that're not familiar with it all they hear is this technology is
16:47
it all they hear is this technology is
16:47
it all they hear is this technology is coming for my job so it's very important
16:50
coming for my job so it's very important
16:50
coming for my job so it's very important also for organizations to think okay
16:53
also for organizations to think okay
16:53
also for organizations to think okay what is the impact that we say that all
16:55
what is the impact that we say that all
16:56
what is the impact that we say that all of a sudden we're going to replace the
16:59
of a sudden we're going to replace the
16:59
of a sudden we're going to replace the you know we're going to take away all
17:01
you know we're going to take away all
17:01
you know we're going to take away all this things that you're doing and we're
17:03
this things that you're doing and we're
17:03
this things that you're doing and we're going to give it to an automated process
17:05
going to give it to an automated process
17:05
going to give it to an automated process behind the scenes right you got to make
17:07
behind the scenes right you got to make
17:07
behind the scenes right you got to make sure that you frame it the right way and
17:09
sure that you frame it the right way and
17:09
sure that you frame it the right way and you got to make sure that you go for
17:11
you got to make sure that you go for
17:11
you got to make sure that you go for those pain points that okay this is
17:13
those pain points that okay this is
17:13
those pain points that okay this is where I'm spending on my time let's
17:16
where I'm spending on my time let's
17:16
where I'm spending on my time let's automate that and we can go back to your
17:18
automate that and we can go back to your
17:18
automate that and we can go back to your uses and say hey you know what now
17:21
uses and say hey you know what now
17:21
uses and say hey you know what now you're not going to spending two hours
17:22
you're not going to spending two hours
17:22
you're not going to spending two hours on this you can spend time on this other
17:25
on this you can spend time on this other
17:25
on this you can spend time on this other things you can maybe stop being late you
17:28
things you can maybe stop being late you
17:28
things you can maybe stop being late you know because all that data entry is
17:30
know because all that data entry is
17:30
know because all that data entry is making you spend two extra hours at the
17:33
making you spend two extra hours at the
17:33
making you spend two extra hours at the end of the day or whatever the case may
17:34
end of the day or whatever the case may
17:34
end of the day or whatever the case may be y but that's important to to take
17:37
be y but that's important to to take
17:37
be y but that's important to to take those concerns or fears into
17:39
those concerns or fears into
17:39
those concerns or fears into consideration as well yep and see if you
17:41
consideration as well yep and see if you
17:41
consideration as well yep and see if you can can relieve some of the actual pains
17:45
can can relieve some of the actual pains
17:45
can can relieve some of the actual pains that the the company has and um because
17:48
that the the company has and um because
17:48
that the the company has and um because I've I've seen how like I said in the in
17:49
I've I've seen how like I said in the in
17:49
I've I've seen how like I said in the in the intro here before we started talking
17:51
the intro here before we started talking
17:51
the intro here before we started talking right that everybody has to have ai
17:54
right that everybody has to have ai
17:54
right that everybody has to have ai right so now a phone a new phone version
17:56
right so now a phone a new phone version
17:56
right so now a phone a new phone version release is like the next version now
18:00
release is like the next version now
18:00
release is like the next version now with AI everything seems to have to have
18:03
with AI everything seems to have to have
18:03
with AI everything seems to have to have ai in it uh but I I suppose what you're
18:06
ai in it uh but I I suppose what you're
18:06
ai in it uh but I I suppose what you're saying if I if I get to paraphrase or
18:08
saying if I if I get to paraphrase or
18:08
saying if I if I get to paraphrase or summarize that you should be careful as
18:11
summarize that you should be careful as
18:11
summarize that you should be careful as a company to uh to not just jump on the
18:15
a company to uh to not just jump on the
18:15
a company to uh to not just jump on the bandwagon and and offer another chatbot
18:17
bandwagon and and offer another chatbot
18:17
bandwagon and and offer another chatbot or some stupid thing that everybody
18:19
or some stupid thing that everybody
18:19
or some stupid thing that everybody already has right yeah correct because
18:22
already has right yeah correct because
18:22
already has right yeah correct because that kind of takes us to one of the
18:23
that kind of takes us to one of the
18:23
that kind of takes us to one of the other questions that people should be
18:25
other questions that people should be
18:25
other questions that people should be asking is do we have enough data and is
18:28
asking is do we have enough data and is
18:28
asking is do we have enough data and is our data
18:29
our data
18:29
our data good enough okay because a phrase that
18:33
good enough okay because a phrase that
18:33
good enough okay because a phrase that um people might might have heard before
18:35
um people might might have heard before
18:35
um people might might have heard before is garbage in garbage out if you provide
18:38
is garbage in garbage out if you provide
18:38
is garbage in garbage out if you provide bad data to your model whether is a you
18:41
bad data to your model whether is a you
18:41
bad data to your model whether is a you know simple model or a gen AI
18:43
know simple model or a gen AI
18:43
know simple model or a gen AI application we've seen it where chatbots
18:45
application we've seen it where chatbots
18:45
application we've seen it where chatbots come up with all these crazy answers and
18:48
come up with all these crazy answers and
18:48
come up with all these crazy answers and things that they you know shouldn't be
18:50
things that they you know shouldn't be
18:50
things that they you know shouldn't be responding so right it's important for
18:53
responding so right it's important for
18:53
responding so right it's important for the organization to take take a pause
18:56
the organization to take take a pause
18:56
the organization to take take a pause and look at their data and do a good
18:58
and look at their data and do a good
18:59
and look at their data and do a good analysis at the end of the day it's
19:00
analysis at the end of the day it's
19:01
analysis at the end of the day it's going to save them time and headaches
19:03
going to save them time and headaches
19:03
going to save them time and headaches and maybe even lawsuits because you know
19:05
and maybe even lawsuits because you know
19:06
and maybe even lawsuits because you know we we've we've had the the story of that
19:08
we we've we've had the the story of that
19:08
we we've we've had the the story of that chatbot that uh or over here there was a
19:12
chatbot that uh or over here there was a
19:12
chatbot that uh or over here there was a story of somebody that uh chat but on a
19:15
story of somebody that uh chat but on a
19:15
story of somebody that uh chat but on a car dealer and um it made the chat but
19:19
car dealer and um it made the chat but
19:19
car dealer and um it made the chat but say that they were GNA get a truck for
19:20
say that they were GNA get a truck for
19:20
say that they were GNA get a truck for like a dollar or something like that
19:22
like a dollar or something like that
19:22
like a dollar or something like that something crazy like that so um you know
19:25
something crazy like that so um you know
19:25
something crazy like that so um you know it's very important to to take a pause
19:27
it's very important to to take a pause
19:27
it's very important to to take a pause and make sure that you take time
19:29
and make sure that you take time
19:29
and make sure that you take time and not just say hey we are AI great
19:32
and not just say hey we are AI great
19:32
and not just say hey we are AI great what issues are you gonna are you going
19:34
what issues are you gonna are you going
19:34
what issues are you gonna are you going to come up with um now that you you're
19:37
to come up with um now that you you're
19:37
to come up with um now that you you're an AI organization so you're an expert
19:40
an AI organization so you're an expert
19:40
an AI organization so you're an expert in this field and I suppose the the
19:42
in this field and I suppose the the
19:42
in this field and I suppose the the answer to the question is of course yes
19:44
answer to the question is of course yes
19:44
answer to the question is of course yes but as a as a company if you don't know
19:46
but as a as a company if you don't know
19:46
but as a as a company if you don't know you should reach out to uh the experts
19:49
you should reach out to uh the experts
19:50
you should reach out to uh the experts in this field who have done this before
19:51
in this field who have done this before
19:52
in this field who have done this before and and and maybe try to listen for the
19:56
and and and maybe try to listen for the
19:56
and and and maybe try to listen for the which what could we be doing and then Al
19:59
which what could we be doing and then Al
19:59
which what could we be doing and then Al invite someone to like look at your
20:00
invite someone to like look at your
20:00
invite someone to like look at your business and understand where can we
20:03
business and understand where can we
20:03
business and understand where can we actually have benefit yeah yeah
20:06
actually have benefit yeah yeah
20:06
actually have benefit yeah yeah definitely you know you need some
20:07
definitely you know you need some
20:07
definitely you know you need some someone that has as we showed in the
20:10
someone that has as we showed in the
20:10
someone that has as we showed in the graph that has that expertise you know
20:12
graph that has that expertise you know
20:12
graph that has that expertise you know data science or developer that has a
20:15
data science or developer that has a
20:15
data science or developer that has a background on that um or somebody that
20:17
background on that um or somebody that
20:17
background on that um or somebody that has implemented this before obviously
20:19
has implemented this before obviously
20:19
has implemented this before obviously with Gen being so new that it's going to
20:22
with Gen being so new that it's going to
20:23
with Gen being so new that it's going to be there's still going to be a little
20:24
be there's still going to be a little
20:24
be there's still going to be a little bit more of risk which is again why I'm
20:27
bit more of risk which is again why I'm
20:27
bit more of risk which is again why I'm showing all these other services have
20:29
showing all these other services have
20:29
showing all these other services have been uh tested and have been have been
20:32
been uh tested and have been have been
20:32
been uh tested and have been have been available for a while because that might
20:34
available for a while because that might
20:34
available for a while because that might be a safer route if if from the business
20:37
be a safer route if if from the business
20:37
be a safer route if if from the business side all they want to say is we are an
20:40
side all they want to say is we are an
20:40
side all they want to say is we are an AI organization or we are using AI that
20:43
AI organization or we are using AI that
20:43
AI organization or we are using AI that is a much safer path for you to be able
20:46
is a much safer path for you to be able
20:46
is a much safer path for you to be able to say that wow okay brilliant thank you
20:48
to say that wow okay brilliant thank you
20:48
to say that wow okay brilliant thank you thank you so much Sam for for kind of
20:51
thank you so much Sam for for kind of
20:51
thank you so much Sam for for kind of summarizing it because 20 minutes it's
20:53
summarizing it because 20 minutes it's
20:53
summarizing it because 20 minutes it's up it's so fast yeah but I appreciate
20:56
up it's so fast yeah but I appreciate
20:56
up it's so fast yeah but I appreciate you being on the show because this was a
20:58
you being on the show because this was a
20:58
you being on the show because this was a really really strong rundown of the
20:59
really really strong rundown of the
20:59
really really strong rundown of the services and and very clear clearly uh
21:03
services and and very clear clearly uh
21:03
services and and very clear clearly uh explained so thank you for being on the
21:05
explained so thank you for being on the
21:05
explained so thank you for being on the show with me today thanks thanks for
21:07
show with me today thanks thanks for
21:07
show with me today thanks thanks for coming me I appreciate it yeah no
21:09
coming me I appreciate it yeah no
21:09
coming me I appreciate it yeah no worries and uh audience I'll see you
21:11
worries and uh audience I'll see you
21:11
worries and uh audience I'll see you again next time for another episode of
21:12
again next time for another episode of
21:13
again next time for another episode of the cloud show
21:15
the cloud show
21:15
the cloud show [Music]
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