August 16, 2019 • 46 mins
Acronyms, abbreviations and just plain confusing terminology are very common in IT and cloud computing. This is our first episode focusing on explaining what everything means and why your business should care.
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Speaker 1 (00:02):
[inaudible].
Speaker 2 (00:03):
Hi, and welcome to Azure for the and mostly as your
focus cloud[inaudible] by theblue silver shift.
Really sit back and cry the loxgroup.
You talk to the cloud.
Speaker 1 (00:19):
[inaudible]
Speaker 2 (00:19):
all right.
Hello everyone.
One happy Friday.
Welcome to another Azure for the[inaudible] podcast.
We have your blue silver shiftcrew here, uh, with a new
addition actually to our podcasttoday.
So, uh, we'll just mention whowe got in the room.
So peg is dropping Mike's.
It's all good.
We're gonna go, we're gonna rollwith it.
Um, so you have your moderatorhere, Mark Wilson.
(00:40):
Uh, we have the partners, Craigslack and John Dobson.
When you guys go hook, go aheadand say hello and go ahead.
Hello.
That was me too.
I'm Craig.
Okay, there we go.
We also have Teresa Starskyjoining us.
I feel like I did.
I got it right, right.
Thank you Terry.
I appreciate the, the, um, theaffirmation.
Okay.
(01:00):
It's delight.
Teresa's actually, uh, somewhatnew at blue silver shift.
She's been, uh, she's joined thecompany for about six weeks now,
and she's gonna just be servinga very, very important purpose
for today.
Um, and she's going to be comoderating and I'll get into the
reason why.
So today's topic and yes, we aregonna talk in a second about
what we have for drinks, uh,before we get into the main
(01:21):
topic.
But, um, so today's topic iscloud terminology.
So there's, when, anytime you'retalking about cloud, especially
in some of our previous episodesof our podcast, there's a lot of
acronyms as a lot of terms thatpeople don't really don't really
know if you haven't been in theindustry for a while.
I know it's taken me quiteawhile to get up to speed on a
lot of these things and there'sstill quite a few that I'm fuzzy
on and, and we get asked all thetime by our clients and other
(01:44):
companies we're speaking to andwhat are these terms?
And so a lot of times peopledon't want to ask.
And uh, you know, we thought itwould provide a handy and handy
way to, uh, you know, just kindof lately a lot in the milk.
This is likely gonna be a twoparter.
So this will be part of one ofthe terms, uh, podcasts we'll do
into part two at some point inthe future.
No pressure.
So Mara, he is just so many ofthem possibly, you know,
(02:07):
everything's changed so fast.
Um, we'll look into the commentssection after the podcast airs
and see if people will ask theperson[inaudible] that'll be a
big list.
Um, so one of the things thatthat Teresa's going to do to
help us out here is if we haveanyone, and I'm looking at John
Right now, uh, that's answeringquestions and there's acronyms
in the question.
Our terms in acronyms in theanswer, in terms of, in the
(02:29):
answer that we might notunderstand.
Theresa's gonna jump in,interrupt them and say, Hey,
what does that mean exactly?
So before we get started, um, asper our usual tradition says
that since it is a Fridayafternoon for us recording this,
uh, we're hanging out in ourlounge in the office and, and
having a couple drinks.
Uh, I myself have a nude vodkasoda nude as the name of the
(02:53):
brand.
Um, and uh, I would have thelabor, it's a, it's a cucumber
mint John to you.
Very manly is a hundred ahundred calories per per cans.
Oh, it made it, that makes iteven more manly there.
Your current concerned aboutyour calories now truck and not
Speaker 3 (03:09):
that's so openly, but it's crazy.
Speaker 2 (03:11):
I appreciate it.
I'm very comfortable.
Um, and then, alright, soTheresa, what do you, what do
you got going on?
Speaker 3 (03:15):
I got a nice glass of old brode red wine from all over
the seat.
Speaker 2 (03:21):
Nice.
John, I think we have theoriginal or the standard for
you.
Standard riot and ginger.
All Ryan can do at homedeviation a little bit.
And then big change for Craig.
Craig, what do you have goingon?
Speaker 4 (03:33):
Well, anybody who's listened to more than one of our
podcasts knows that I like myscotch and my whiskey.
Um, so today I've got a niceCanadian whiskey, but I've added
some coke, zero to it.
Speaker 2 (03:45):
So sort of the words coke and whiskey.
Um, Craig's always got thereally Nice Scotch, but, and I
will say I'm a bit this way.
Nobody commented on my, uh, my,my Microsoft cloud, Microsoft
Canada cloud socks that I'mwearing today.
[inaudible] we have half thecompany where Microsoft saw
Tate's called Microsoft socks tothe awesome.
(04:06):
I was going to say something Iforgot to, sorry.
Speaker 3 (04:08):
Oh, it seems that this company is not jumping on
the short pants, then buy again.
And I'm okay with that.
Speaker 2 (04:16):
Oh, short pants for me.
Um, there we go.
Anyways.
All right.
Diverse.
I just like, as I was sittinghere, I noticed, I'm like,
nobody's commented.
Um, all right, well let's, uh,let's jump in.
Okay.
So today we're talking about, Ohyes.
Cheers everybody.
Thank you, Teresa.
Cheers.
Speaker 3 (04:36):
Okay.
So,
Speaker 2 (04:39):
um, our first question, I'm going to kind of
put this into two spots and I'mgonna Throw, I'm going to ask
Jon and correct to kind of, uh,double taking this answer.
So,
Speaker 4 (04:50):
okay,
Speaker 2 (04:51):
I'm start basic.
What is the cloud?
Before, before you startedanswering that, what is the
cloud and then what are thetypes of clouds?
So here I have private, public,hybrid, multi.
So what is a cloud and what doesit different through private,
public, hybrid and voltage.
Speaker 4 (05:07):
Oh, you're handing it to me, Craig, over to you to
start.
Well, what is the cloud?
That's a pretty broad questionbecause it means so many things
to so many people.
Speaker 2 (05:15):
Well it's got like a ten second shot, like a elevator
pitch.
Okay.
Some people think it's theiridea,
Speaker 4 (05:19):
iCloud, Dropbox, whatever.
But the cloud essentially camefrom the term where you know, an
old architecture diagram aspeople that represent the, the
Internet as a cloud on anarchitecture diagram.
So you're communicating trafficout to the Internet.
It would just go to this bigcloud on the diagram.
So that's as far as Iunderstand.
(05:41):
That's where the term, how itcame from.
So it really means anythingthat's stored or processed or
running up in the Internet.
And my definition to it is justan add on would be that, um,
I've heard it referred to assomeone else's computer, uh,
your computer
Speaker 3 (05:58):
at someone else's.
And on the far end, Eric Schmidtfrom the Google, I don't know if
he's still the CEO of Google ornot, but he was the guy that
actually coined the term 15years ago.
Oh Wow.
Yeah.
So it is, as Craig said, it'snot here.
Speaker 4 (06:15):
Okay.
And then the different types ofcloud.
So yeah.
So different types of clouds.
The second part of yourquestion.
So I'll kinda just rattle themoff.
And John said that there's more,so he'll probably add more.
Right.
So private cloud is a conceptthat's been around for decades
where it's, I'm gonna use aterminology that's very it
(06:35):
centric, but it's co-location.
So it means, uh, in the, youknow, many years ago you would
buy a server, you maybe didn'thave a data center that, uh, you
were hosting yourself on site.
You would buy that server, putit in somebody else's data
center and they would eithermanage it for you or you would
manage it remotely, whatever.
(06:56):
So that has been rebranded asprivate cloud.
So it's because it's either yourequipment or it could be still
somebody else's equipment asJohn just said.
Um, but it's in a data centerthat's not yours, but it's not,
uh, it's like a privateconnection, private, everything
like that.
Speaker 3 (07:13):
It could be their on premises data closet.
Yes.
And I think that they do itbecause they feel jealous of the
big guys having a big cloudpublic cloud.
And then therefore they say, oh,I've got a private cloud.
Where do you keep that privatecloud?
Speaker 4 (07:27):
Yeah.
And they can call it that onlybecause it's connected over an
internet connection or some sortof network connection is not
directly located on site withtheir, where their main
operation is.
Um, public cloud is whateverybody would refer to as, you
know, the Google, the Amazon andthe Microsoft Azure, um,
(07:48):
environments.
Those are public cloudenvironments that are open to
all, they've got essentiallyinfinite scalability, uh, many
geographic locations, uh, aroundthe world.
Uh, and they, they provide manymore services than a private
cloud can.
I don't know if you want to addhere.
Speaker 3 (08:06):
Yeah.
And just to go back a bit on, uh, private cloud, um, technically
for what Facebook has and what,um, Google has are private
because they're for theirinternal systems cause they're
massive computing areas.
Yup.
And they're their own things.
They're not open to the publicand that's a good point.
Yeah.
(08:26):
And they're also private is likewe said, closets and someone
else's computer.
Hybrid.
You want to take this one, John?
Uh, I have a mixture of, um, onpremises systems, uh, and um,
meaning I've still have my oldmindset, my old computing style.
(08:48):
And I've also started my cloudjourney, some things in Azure.
So I've been a mixture ofenvironments, probably
Speaker 4 (08:55):
very common.
Oh, that's more common than not.
Absolutely.
Yeah.
There's very few pure cloud,whether that's private or public
cloud companies.
Correct.
Speaker 3 (09:06):
Correct.
Multi, multi cloud.
Uh, is that more of a strategythan anything else?
Because a, I have found acompany, um, one of the design
principles that an architectaspires to is to have, uh, I
want the data in all threeclouds.
And one of the reasons is theydon't want to be held ransom by
any of the three public cloudproviders, the big ones.
(09:28):
So they try to, um, have astrategy of multi-cloud for
their service offerings at it's,um, got diminishing returns
though.
Was that because, um, the effortyou put in to designing and
getting a solution like thatworking and it only really works
for specific use cases.
(09:48):
If, for example, the padsofferings on all these clouds
cracked your couch are veryspecific to the cloud and you
would have to have differentpaths, solutions in place per
public cloud that you're using.
So we often hear customerssaying, oh, we're going to have
a public cloud, we're going tohave a, um, a multicloud
(10:10):
strategy and it's just y and[inaudible].
I mean, it's the diminishingreturns component of it.
Yeah, correct.
So if you're a business payingfor that, you, you'd be
questioning why someone wouldactually be doing that.
So I'll just give you anexample.
Uh, Netflix, uh, has an Uber andthose guys have an Amazon
(10:34):
component and they would havemulti regions within the same
provider, but they wouldn't haveit off into a different public
cloud because it's just toohard.
There's enough fail overcapability and redundancy inside
of public cloud offerings thatyou don't have to go that way.
So a traditional business wouldreally never have to go
multi-cloud ever if you're beingtold that you should question
(10:58):
it.
Speaker 4 (10:59):
Sony announced recently that they're, so if you
will, if you wanted to like areally good example, um, in the
future to give, like you justgave for Netflix or Uber using,
uh, AWS as you can say, Sony isgoing to run all of their online
gaming on Azure.
Yeah.
So that's, that was a huge, hugething to make Azure much more of
a household name in the verynear future.
(11:19):
Yeah.
This doesn't actually definemulticloud, but there's, just to
touch on it briefly, there's anumber of hidden costs that come
with going multi-cloud.
So if you've got, you know,services that are talking to
each other, but they're goingfrom Google to Amazon to Azure
or just the two of them you'regoing to have.
And this is going to be anotherterm that we'll define later.
(11:40):
Egress network charges.
Yes.
So we'll define that in aminute, but uh, that you're,
you're, you've got networkbandwidth charges, but then
you're also on top of that,you've got management costs.
So your, your team now has tohave multiple, um, skill sets to
manage different clouds causethey're all managed different
ways.
There's no single.
(12:01):
Yeah.
Speaker 3 (12:01):
And Are you, are they all active, active, active, or
are they active?
Passive.
Passive.
Oh No, that's the case.
Well, meaning that one is liveand the other two are sitting
dormant.
Yeah.
But you got to pay for them.
Speaker 4 (12:11):
Yeah, that's true.
So there's the diminishingreturns again.
Yep.
And actually something Johnmentioned too, about it being a
strategy.
Multicloud is a strategy becausecompanies don't want to be held
captive by any one vendor.
If, you know, Microsoft orGoogle or whoever decides to
dramatically change theirservice offering or their prices
(12:32):
or whatever, which it'd be hardfor them to do because it's a
very competitive market.
So they're all basically quitematte price matching.
But, um, it's, it brings upanother term that I wanted to
quickly define cause it'srelevant here as vendor lock in.
So, um, it's very relevant inthe cloud.
It's also relevant in, you know,on Prem.
(12:52):
But, um, the, the concern ofbeing locked into a vendor and
you, you don't have a choice to,to ever move off without
reinvesting and re redevelopingor redesigning your entire
system.
So that's why they're kind ofadopting a multicloud strategy
so that they can just at a flipof a switch, in theory, switched
(13:13):
to another cloud vendor withoutany implications.
W uh, design criteria becauseyeah,
Speaker 3 (13:19):
they lock in on ERP, CRM, uh, where they're working,
who their partners are in life,everything.
We create the acronyms toshorten the conversation.
That's why that's the purpose ofthis to, okay.
Speaker 2 (13:38):
I think we spent enough time on it as well.
We're going to jump into thenext one, so I know I'm not the
moderator.
Next question.
Ah, you know, there's a lot ofdifferent permutations of the
cloud and I feel like I've got apretty good handle on, on these.
I feel like if you asked me, Icould keep the definition, but
I'm going to, uh, I'm gonnathrow it over to you guys.
Oh, you should try it.
I want to hear yours becauseeverybody has different
interpretations, so.
(13:59):
Alright.
Alright.
So, um, we have a, so here'ssome really common ones that
have been coming out.
So we have[inaudible] pads, SASdrives as, as, so I think it's
as, I think it's how youpronounce it.
So these are all acronyms.
Uh, so IAAS is I a s.
Um, so that's, uh,infrastructure as a service.
(14:20):
Sorry.
Um, we have, uh, paths, platformas a service.
We have saas software as aservice.
Yes, you can.
Yeah.
So I'm going to go back throughthe mall.
Um, uh, so drowsy is actually abelieved disaster recovery as a
service.
Yeah.
Okay.
Uh, and then Zass I think it'sjust anything as a service as x,
a s yeah.
(14:40):
X, XP, anything.
Yeah.
Okay.
So I'm actually going to startwith Sass cause I think that
that's what people have the mostexposure to.
Everyone needs a little sass intheir life.
Everyone has a lot of Saas inlife.
Actually, I probably ain't evenlisten to this as well.
Speaker 3 (14:55):
Nice.
Speaker 2 (14:56):
Um, so SAS software as a service is really any kind
of, uh, any application thatyou're accessing through a web
browser.
So if you are logging into anaccounting software through your
web browser, that's going to bea software as a service.
Uh, you could, you could even,you could consider, um,
something like Spotify.
Uh, you know, and so that's aconsumer based SAS product.
(15:18):
Um, you know, real.
So yeah.
So anything that that is, thatis access through a web browser
is going to be
Speaker 3 (15:24):
all those office three 65 as a SAS product office
three 65.
I'm going to get into somethings that are listed in here
that people think are pads, butI actually consider them to be
SAS.
Speaker 2 (15:34):
That's actually, so that was going to be one of my
questions.
I'm interested in that.
Um, so, so SAS is really, mostof the time when you're, most
people's experience with thecloud is going to be through SAS
I believe.
Um, I as so infrastructures as aservice.
That's really when your ma, Imean for our purposes and from
my experience, it's mainly beenwhen you're putting an
application onto the cloud, butyou're not really, uh,
(15:58):
refactoring the application,you're really just re hosting
the application.
So a lot of the times we'redoing a lift and shift of an
application that may be sittingon an on premises server.
So you have some sort ofaccounting software, um, or
something that's sitting inside,uh, sitting on your server
inside of your office or insideof a private cloud.
Um, and it, you know, might besitting on a, you know, your
(16:19):
windows server or your SQLserver and you would then lift
that application up, shiftedover the cloud, drop it down.
And that is infrastructure as aservice.
So you're not really changingthe application at all.
Um, you're really just rehosting it.
Yeah.
Um, same old, same old, sameold, same old.
(16:39):
So you're just doing the samethings a lot.
You know, I've actually spokento a lot of companies, well not
really, not a lot, but a fewthat have said to me, you know,
we have this application, it'ssitting on our server.
We can't put it in the cloudbecause it won't work in the
cloud a lot of the time.
I think that's because they'reexpecting to have to redevelop
it into, uh, into, uh, a SASproduct or, or put it on a, on a
pass, um, uh, infrastructurewhen really we can have any
(17:02):
working exactly the same way asit does now.
Just living in the cloud.
Uh, PAS is a platform as aservice.
So really that's when you'relooking at rebuilding the
application.
If you're looking at migrating,um, you know, you're rebuilding
the application in the cloud.
So, um, this is where I start toget a little bit, uh, I know
(17:22):
that there's a lot ofimplications to pass and it's a
much, much more powerful.
Speaker 3 (17:26):
So if you had sequel in an I s so I for structure as
a service.
So you had a VM with sequel init and then you moved it to a
pass sequel.
You don't actually get the Oos,you just get the SQL service,
but you manage it through thesame type of console.
I'm just making imaginary marksin the air.
So we've got sequel, which is atype of database VM in their
(17:49):
virtual machine.
We had o s operating system andI'm like, what's wrong with me?
I have the question because markjust said, you know, sometimes
you can just lift and shift todo the[inaudible] approach, but
there are obviously times whereyou do have to rebuild an
application to the past.
I don't know if calling it anapproach is the correct thing,
(18:09):
but what's the deciding factorbetween whether you can do an I
as versus having to do a passbecause I assume rebuilding is a
lot more costly and people wouldbe probably tending to want to
do an I as for cost purposes atleast.
So how much money you want tospend to do the refactoring,
whether or not that's going toactually give your business
significant value in doing it.
(18:31):
And because, because when youmove to pass, it's going to
reduce your infrastructure andincrease your ability to scale.
So if you've got a businessthat's going to scale up, it
would be, uh, advantageous totake an Ios SQL server, which is
a traditional server and convertit to pass because then you'll
scale it up and you'll get muchbetter value overall out of it.
(18:51):
So it takes some longterm viewto make a, yeah.
Worthwhile.
Correct.
It doesn't often, it's anevolution to, it's a journey.
Some, some applications can't doit because they're too old.
They just will not talk tosequel or talk to.
And it's not worth theinvestment.
That's where the word legacycomes in.
That's another[inaudible].
Speaker 2 (19:14):
Alright.
Um, so we have drowsy, sodisaster recovery as a service.
And I mean, I think that'sreally just looking at, you
know, making sure that you haveyour disaster recovery
infrastructure, um, set up andestablished so that when, if you
know, if an event is occurring,then you have a mix of, I'm
assuming automated processesthat are going to um, you know,
(19:35):
spin up your infrastructure andanother, uh, Geo low, uh, you
know, geographical location.
Um, but then also when you'relooking at the, at the service
of disaster recovery, it'sreally, um, new, constantly
monitoring, updating, adaptingto new challenges, making sure
that you have, um, you know, anykind of, uh, changes to your
systems or processes that arebeing required, uh, for, you
(19:58):
know, new infrastructure changeswithin the cloud hosting
provider or within yourapplications that everything is,
you know, set up and ready sothat if something should occur,
you are ready to take advantageof those disasters.
Speaker 3 (20:08):
It's recovery service .
And while a disaster recovery asa service works with on premises
applications to the cloud andfrom the cloud to the cloud to a
different cloud, to a differentregion within the same provider,
um, it never really got, um, anya strength behind it or was
popular as a service untilpublic clouds came about.
(20:28):
So in other words, it is a typeof service that was often, uh,
preconfigured locally a onpremises in a private data
center.
And then that's it.
It's, it just took off with, um,public cloud and Dra as a
service is almost, you can alsodo it as like an outsourced
service that you're subscribingto
Speaker 4 (20:48):
in a company, takes care of your entire Dr Strategy,
your actual backup and therecoverability and doing all the
testing of your environment.
Speaker 3 (20:57):
So, which is something we do.
Is there a disaster scenariowhere it would not be
recoverable?
For instance, we talk about thecloud, but everything still
comes down to a machinesomewhere, right?
That would not be recoverable.
Is there an instance or was thata quite extreme situation?
Yeah.
So I think when you're sayingeverything comes down to a
(21:19):
machine is the end user that'susing that system rather where
it's stored.
So you can architect it so thatit can wish them, you know, all
of North America drops off themap.
You can still keep on running,right?
So you could, you couldarchitect to that level, but it
depends on the kind of business.
So if you have a hightransactional business where
(21:39):
people are processing creditcards all the time, and if that
system's not down, they can'tcomplete the sale.
The customer walks out the door.
You have to design a solutionfor that to be able to do it.
So that's not disaster recovery.
That's high availability,business continuity, business
continuity, high visibilitywhere you're going to actually
have these systems working,active, active as partners in
(22:01):
different physical regions withredundant connections,
crossovers, whatever you want tocall them, into that same
system.
Right?
I can't go here.
I'll go there.
And, and companies know it'sworthwhile to pay for that
because they know the cost ofdowntime.
They know that at say, um, theapple store, if they can't
process sales each, each applestore makes$50 million a
quarter.
(22:21):
A quarter.
Yeah.
And um, so if you had a businessthat made$50 million a quarter,
you would want a redundant,you'd always want that cash
register rotting.
Probably very small cost.
Oh, compared to there.
But sometimes it's not a smallcost compared to what it is.
So it's not, it doesn't makesense to do it.
The corner store actually inpackets of bubble gum,
Speaker 4 (22:42):
I was just going to make that exact example because
the other day I went to thecorner store just down the
street and their Internetconnection was down and he was
turning away business because hecouldn't process credit cards
cause all of his credit cardstuff goes out over the
internet.
And he was visibly frustrated as[inaudible]
Speaker 3 (22:59):
people were leaving.
He's like, I'm losing, I lookedinto his phone.
I don't know cause that's[inaudible]
Speaker 4 (23:04):
if you look at the costs and say another Internet
connection, 50 or a hundredbucks a month, um, if he lost$50
in that one day, he would havepaid for that back, that
redundancy there.
So I think it applies to everybusiness.
They just have to run their owncost benefit analysis to figure
out does it make sense for thatcost?
Cause it doesn't, it's in somecases it can be very expensive
(23:25):
to get that fully redundantsolution.
And if, if the downtime doesn'tequate to the dollars, it's
gonna cost to, um, to put thatredundancy in.
Then many companies will justaccept that risk that yeah.
Speaker 3 (23:39):
And they'll have a recovery time objective.
That's another definition youdidn't use it.
And in RPO is recovery pointobjective.
So again, when you've got atransactional system, that means
it's journaling as opposed to apoint in time.
Just bring me back to lastTuesday.
Just bring me back to last nightat seven o'clock.
Most businesses are covered bythat type of recovery point
(24:02):
objective.
But then they also have arecovery time objective that
says, bring me back to thisparticular time.
It sounds like we're at the[inaudible].
Speaker 2 (24:12):
I don't know if our bikes aren't getting out, but
there's a seagull on our balconytoday, but the seagull is
tripping out.
I should've actually mentionedwhen we started, I, I didn't,
but uh, Theresa is, um, actuallythe person who is has to listen
to us later and go through the,go through the podcast and pull
(24:34):
out some of the contents.
So a, she actually helped toinspire this a, this particular
topic and um, Yup, I will saythat sometimes some of the auto
transcription, so some of thepodcasts that she's pointed out
to me have been pretty funny.
Um, I think that's thetechnology that still catch it
up.
(24:55):
Um, all right, last one on this.
This specific topic is as oranything as a service and I w I
mean it's, well, yeah, cause DRMas a service
Speaker 3 (25:04):
doesn't really fall into the same categories as I,
as pals and SAS.
So it's really, I call it excess, but you know, as sounds
cooler, I think so.
I don't know how the properpronunciation of it, but it's x
meaning anything as a service.
So I think in the future as weget more and more things served
(25:24):
up from the cloud, they are as aservice.
Right?
So it's, yeah, we offergovernance as a service.
There you go.
Yup.
Temple.
And I'm sure this time nextmonth or two months from now,
we'll have another service inthere that we're streamlining
pricing as a service.
Why don't we define what cloudgovernance is while we're,
(25:46):
you're going to talk about itlater.
Okay.
No, no, that's, that's onto theother list.
Okay.
Speaker 2 (25:50):
Well, I mean, so we're going to, I mean, we're
going to read up into, we'regoing to jump into a lightning
round while we want.
Oh yeah.
Okay.
Well let's hold off on that fora second.
Cause it's actually the toptalks.
It's actually the top of thelist in the lightning round.
Okay.
So we're going into a lightninground now.
So we're going to talk about,I'm just gonna throw some terms
and I'm going to point jeopardymusic.
(26:10):
Okay.
And you guys, how long do wehave?
You have like 15 seconds.
15.
That's easy.
No, no, no, no.
Per, per, per, per term.
Okay.
15 seconds per term.
Cause that'd be cause you knowhow many things you could rattle
off.
Actually that's what you shoulddo.
I had the bonus.
We should actually have acountdown timer.
If 30 Sab left the most amountof acronyms.
Now you can define it.
(26:31):
Correct.
Anyway, sorry.
We should actually be inseparate booth.
I mean I may be able to, to comeup with something like that
right now on the fly too.
We do have a long list here likewe got, um, hey, it doesn't
matter.
Okay.
Okay.
So let's, let's take a look.
We're going to jump into relatearound a little bit different
than the normal lightning round.
You got, uh, 15 seconds.
I'm might give you 20 if youreally need it.
(26:51):
But we're going to start offwith cloud governance and we're
going to go to John.
Go ahead.
Speaker 3 (26:56):
Cloud governance.
Well, how we govern our lives ishow you govern your cloud.
Meaning, uh, you use a solutionevery day that says, I'm not
going to spend this much money.
I'm not going to do these thingscause they're harmful to me.
I'm not going to do somethingelse.
You have a governance system andcloud governance is your ability
to manage and the policies andprocedures that you're going to
(27:17):
use to actually do it.
Speaker 2 (27:19):
It's good enough in 15 seconds.
That's a good analogy.
You mean it was about, it wasunder 2016 five given the
buzzer.
I do the same one or no, don'tworry.
Maybe a new one.
So, uh, Craig Cloud CustodianCloud Custodian
Speaker 4 (27:33):
is somebody who's monitoring and maintaining the,
enforcing the governance, makingsure that those policies that
John mentioned are adhered to.
Tracking the cost, uh, lookingfor workload optimization
opportunities and so on.
Speaker 2 (27:48):
Yeah.
Pretty good.
15 on the dot.
Oh my God.
He didn't eat by 1.5 second.
Alright, John,
Speaker 4 (27:55):
for all
Speaker 3 (27:56):
sprawl is a, when you don't have cloud governance,
your systems will go out ofcontrol like weeds in a garden.
Speaker 4 (28:03):
Okay,
Speaker 2 (28:04):
awesome.
Under eight seconds.
You don't, you don't, you don'thave to just under 20 seconds
wind up, be informative as well.
Needs to be as quick aspossible.
Um, clouds
Speaker 4 (28:16):
brawl is really related to like people just not
controlling the amount ofresources they're spinning up
and not deleting them.
Yeah.
Speaker 3 (28:26):
When you say that you mean things like people creating
files and putting them somewhereand forgetting about them
Speaker 2 (28:31):
that files is an example.
But we'll do that with systems.
Speaker 4 (28:36):
Virtual machines, servers, networks, just
resources a yeah, if you don'thave a process in place to
contain that sprawl occurs, thesystems, et cetera.
Okay.
Speaker 2 (28:46):
Okay.
So next is um, I'm going to giveyou the term, but I'm going to
pair it with another term thatis used often.
I've used it in the past.
It's incorrect.
Um, so on premises definitedefinition and why is on
premise, why does that make nosense?
And who went last, correct.
You went last?
No, no, John, this is one of mypet peeves.
(29:08):
I should probably get to thiswith Greg.
You're up.
Speaker 4 (29:11):
And, and people who worked with me long enough know
that I pointed out all the timebecause it's a common mistake
and it so on prem or onpremises.
Um, the proper terminology iswhen you've got servers or
equipment located at your mainprimary res, uh, not residents,
primary office of operation andit's running the systems there.
(29:34):
I know I'm going over 15seconds.
That's okay.
On premise, which is, well justthe word premise is like the
premise
Speaker 2 (29:41):
of doing something right.
That's, yeah.
Yes.
So on premise it doesn't,doesn't make any, it's not
proper English.
So, and now that I'm an Englishmajor, that'll be more
Speaker 3 (29:53):
or English Nazi, my boss just went up and my
estimation over that.
Wow.
I must have been pretty low tobegin with.
I was an English major too.
Well I wasn't, no, he said hewasn't[inaudible] that's why I
was pointing at, you know,discovering the distinction.
(30:14):
So oftentimes in our industrypeople say, Oh, and then you
just move your stuff from youron premise stuff up to the
cloud.
And I keep thinking to myself,so right now, and I'm going to
try and boil it down even betterthan Craig did.
We are sitting in a premise.
Yes.
And for me to say the sky isblue is a premise.
That's a good way to put it.
So the point is is that peopleget that wrong.
(30:36):
And,
Speaker 2 (30:37):
and I have an admission to make, I'd say
probably until about five yearsago, I was saying on premise all
the time too.
So I've said many times there'sno shame to be
Speaker 3 (30:48):
but, and, and, but the real question is, do you
correct some of them?
No.
Could you say that?
Do you correct your customer?
Could you say that?
Oh yeah, yeah, you do.
But do you correct a customer?
Would you correct a customer orwith the customers?
Speaker 2 (31:07):
You don't want to hold, you don't want them to,
you don't be saying the wrongthing to other people, right.
It's like when somebody hassomething stuck in their teeth,
you want to help them out.
So, so could you, could you say,could you say that like the
premises is the lounge in ouroffice and the premise is that
we're here to do the podcast?
Yes.
That's okay.
Summation.
All right.
Um, so John, a new throw, atricky one over to you.
And I'm skipping, I'm skippingsome here cause I wanna this is
(31:30):
like tricky ones.
I want, I want to know, oh, whatis machine learning?
What is AI?
Oh my roots between she, betweenmachine learning and AI.
And I'm not going, I'm notgiving you a 15 second timer.
Okay.
So this one we're both, I'm
Speaker 3 (31:47):
going to answer in on.
So here we go.
Artificial intelligence is ascience fiction, uh, concept.
So skynet as an example, uh,that's where it comes from
originally.
Meaning that you can actuallygive the computer, it's an
entire domain and machinelearning is part of that domain.
(32:08):
And I think deep learning is theother part of that domain.
They call it deep learning.
Um, and so AI is where, um, youare going to have your data
centrally stored and hopefullyin the cloud.
And that should be your firststep on your digital
transformation.
I don't know if that's a wordthat we gotta define.
Probably the second thing is, isyou, once you're at that spot,
(32:30):
meaning your data's not all overthe place or unknown to you, you
actually can apply certainpublic cloud AI concepts like
machine learning into a, to lookat your data.
And what it's gonna do is it'sgonna find outliers.
It's gonna find it's gonna,you're gonna work with it.
So a layman can work with it.
You don't have to be a datascientist to actually determine
(32:52):
where, uh, to train the data.
Meaning look at this.
Y'all train the algorithm and tomodel the data better so that
you can actually ask itquestions on insights like, Uhm,
questions, any, any type ofquestion.
All like, instead of having anaccountant look at all your data
and say, hey, you got to raisethe price here and drop it
(33:14):
there.
You can actually have an AI thatnever calls in sick, that is
continually looking at your dataor a machine learning algorithm
that is essentially pays you gotell you the exact same good
advice, but probably on a muchgreater scale if you have a huge
volume of data and the largerthe volume sets of data, the
(33:38):
better.
And um, here's the reality.
That type of technology is gonnamake humans live to be 200 years
old because that kind oftechnology is going to look at
your data as Apple's collectingit as Google's collecting it and
whatever.
And it is gonna say, Hey, thistype of activity or whatever
you're doing needs to stop orincrease or otherwise, or you
(34:00):
need to go for an exam onsomething so that, that's where
it's manifesting itself rightnow.
So we wouldn't be able to getthere with our biological
brains, believe it or not.
But AI can actually push youthere if it can do that for the
human body to 200 years,essentially double plus the
lifespan.
Imagine what it can do for yourbusiness.
Speaker 4 (34:22):
I don't know if I can even add to that cause that
that's, that is a greatdefinition.
I just read, I I, I agree thatml or machine learning is a
component of artificialintelligence because artificial
intelligence spans manydifferent spaces.
It can looting science fiction.
Yeah.
And so if we talk about bots forexample, where you know chat
(34:46):
bots, which is probablysomething you need to define as
well, but it's, you could be ona website and you get a little
pop up window that says, Hey ourcustomer service people are here
to help.
And you're interacting with avirtual customer service agent
that does not even potentiallymost likely a real person.
Um, cause they've built thesecommon things.
(35:07):
It's using artificialintelligence, not necessarily
machine learning.
They know that if a certainquestion or, or it can figure
out that it's relating to asimilar question, it's in their
database, it will spit out acertain answer.
Right.
So that's what I would define aspart of artificial intelligence.
Machine learning is, is thewhole data side.
And, and being able to get it toa point where you can create an
(35:31):
API that is, and by a machinelearning algorithm you send it a
single data point, um, and itwill be able to predict
Speaker 3 (35:38):
something on the other end of it.
So that is, yeah, and businesseswant the predictability.
So again, if you think aboutbusinesses from 20 years ago,
they were in beyond, they werelike driving a car at night with
the lights.
They were, okay, this justhappened, let's turn.
Okay, let's do this.
And AI going to provide thatpredictability moving forward so
(36:01):
that they've got room to grow,ability to plan, et cetera.
So good, good summation.
Speaker 2 (36:08):
One of the ways that I have been kind of as I'm been
trying to wrap my mind aroundthe differences, and I'm going
to use, you mentioned likeliving to 200 so I'm going to
piggyback on that as like a, onthe health thing, the health
idea.
So the way that I've beenlooking at machine learning, and
I think that I think they'retalking about machine learning
as a component of AI is kindawill help a lot of people.
(36:28):
Um, I know that it definitelyhelps me.
I've always been looking at itor recently I've been looking at
it as like, uh, so, okay, so onthe health thing, like medical
imaging, so you program amachine learning system to look
at the, you know, a brain scanof 10 million people or whatever
and you say, um, every like lookat at the brains of 10 million
(36:52):
or have x amount of people whohave had a stroke and identify
at, identify a commoncharacteristics of those brain
scans and then spit outsomething at the end that's
going to tell you, yeah, this isa common thing that'll, that'll
cost Australia works.
Yeah.
Whereas AI, I've been kind oflooking at it as like you would
build a system where you give ita a same amount of brain scans
(37:15):
and it decides for itself whatis causing what and what is, you
know what it's, cause it's our,it's an artificial intelligence
is learning off of itself.
So it's not necessarily, you'renot telling it.
I identify these characteristicswith the in this magic to this
result.
You're saying look at thesescans and then seeing what the
heck comes out the other.
Speaker 3 (37:35):
It's bringing up parallel processing.
So again, it's to go on yourpoint there.
I was thinking about it asyou're saying it, we, it wasn't
available before public cloud.
So same way as a service wasn'tavailable.
AI Really wasn't available.
You could go and buy IBM Watson,but then you've got to have a
(37:56):
data center, a private cloudsync the cost into it and then
throw your data at it.
It's very specific, exactly howyou do it.
And even the amount of data,something like that could look
at was was limited by yourcapacity AI in the cloud.
The more data I have, the morecan look at it all at once.
So the parallel processingcapabilities are huge.
(38:18):
I have another, uh, um, call itan analogy or otherwise where AI
traditionally and historically Iguess has been more a lot of if
then else statements.
So think of it, you're thinkingof a autonomous car, right?
A self driving car.
Um,
Speaker 4 (38:36):
you know, it's a car driving straight down the road.
It needs to make millions ofdecisions at a time.
Okay, so simple is the redtraffic light red stop.
So if the traffic light appears,red must come to a stop at the
line.
If the traffic lights screen,keep going.
So keep it simple with that.
Whereas with, when you add inthe machine learning element,
(39:00):
you have now all theseautonomous cars, millions of
them around the world feedingtheir data to the cloud because
the cloud has infinite capacity.
That data can now be analyzedand those, um, if then l
statements can be, uh, um,manipulated and changed over
time to evolve, to become moreefficient and more intelligent.
(39:23):
So now let's just take anothersimple example.
So if the lights red stop, butnow because of say a crash
happened, that it ran over apedestrian cause somebody who
was crossing the light on a red,the light was green, the car
started going, but theautonomous car drove over
somebody.
Now that machine learning, thatAI model can add in if the light
(39:46):
turns green go.
But Oh, if there's a personstanding in front of you, don't
go.
So it's learned that Ms.
Dot.
AI Model has now learned fromthe machine learning data and
the all the masses of data.
Speaker 3 (40:00):
So I don't want to take this too far away from, um,
definitions, definitions, butfor the casual listener taking
in these AI, uh, stories, is itliable to be a Frankenstein's
monster one day?
We're all a biological computer.
(40:22):
We are a biological computer,right?
Yeah.
Or, or a computer as a, uh,artificial, a facsimile of us,
correct.
Of our brain and the way ourbrain works and everything.
The, with what Craig justexplained, we do that.
Like, I'm doing that right nowand I'm thinking about other
things.
I'm thinking about what I'mgoing to say.
I'm, I'm breathing, I've got anautonomous nervous system that's
(40:44):
actually going and firing aswell all at once.
Computers.
So with what Craig started,we're in the early days of, but
the thing that computers havetaught us is that they can
accelerate and go up quitequickly.
So we've got self driving cars,we're going to have packages
delivered, you're going to ordersomething and something's going
to come in a in a drone and dropoff and it's going to be
(41:04):
reliable.
It's going to be a solidbusiness model.
We're probably 10 years awayfrom at least from that being
mainstream.
Like I mean more people drivingautonomous cars than not.
We have an employee here that'sgot an autonomous driving car as
well.
So it's already starting tohappen.
Um, I think skynet and theconcepts that are in popular
(41:26):
culture or, um, I'm not surewhat the, the alien or the AI
was called in 2001 but that oneis, well, they're, they're out
from that by quite a bit.
Meaning that I can actuallyponder while do I want to wear
different types of clothes orwhat do I want to do tomorrow?
A computer has no concept ofthat.
Speaker 2 (41:48):
So one of my favorites Gov of of quote
unquote evil AI because it can'treally be evil.
Um, oh this is my, this is myfavorite, is my favorite example
of, of a way that that AI wipesout humanity.
So, oh my gosh.
So hasn't got a conscience.
So there's, so there's, there'sa, there's a business and
(42:09):
they're an a and they're in thebusiness of handwritten letters
cause people were opening uphandwritten letters more than
the open up something that isclearly printed.
So they actually build themachine.
It's a very small machine and ithas a pen on it and you can, you
buy this machine, it'll handwrite your letters for you hand
write the envelope and he'dwrite the letter.
A person didn't write it, themachine wrote it.
Um, so they're trying to get itto that.
It looks better.
(42:29):
So they're trying to get morerealistic handwriting out of
this thing.
So they connected to theInternet.
And they tell it, learn how tohand write better.
So it, so it starts scouring allthese handwritten images and
learn, but all of a sudden forsome reason, for some special
case, this one instance of thishandwriting technology, this one
algorithm to look out there andlearn how to write handwriting
(42:49):
better.
That's the singularity andinspires conscious.
Then realizes, hey, if I breakout of my confines, I can hand
write better and it has onemission hand write better.
So it because it can learn sofast, it learns very, very short
amount of time how to developmuch, much smaller technology.
All of a sudden it's buildingnanobots and it realizes, hey,
(43:13):
humans are not actually nanobotsbeing like molecular, regular
local machines that can do thewriting itself.
Rarely humans are actually goingto stop me because they don't
want to die, but they're goingto stop my mission of writing
better notes.
So it, you know, basically wipesout.
It uses Naoto boss to wipe outthe whole planet and it uses
(43:35):
needle bops to reconfigure allthe matter to, to build these
little machines that itrealizes, hey, amount of matter.
So then built rockets to blastoff to the moon, back to Mars,
then to other solar systems thatthe entire galaxy and the entire
galaxy is covered in these tinylittle machines that are writing
the same letter over and overand over again.
Is that from something you getbetter at?
(43:56):
Right.
Actually be a black mirrorepisode.
Ah, yeah.
I'd read that as like a shortstory.
Oh yeah.
That's awesome.
Perfect example, because itdoesn't care.
There's no more reality.
It's not like good or evil orwhatever.
So skynet doing exoskeletonswith skin on it, writing better
letters.
Correct.
That's awesome.
That's my favorite AI story.
That's a good way to go.
(44:18):
Papered and beautifulhandwriting.
I guess there's worse ways.
I'm all right.
I think that we're about out oftime here, so we're going to cut
it off for round one.
We're going to do round twoagain.
I blamed myself a little bit forgoing off of my AI tangent.
Um, but we're going to do roundtwo.
We still call rise eyes.
Sorry.
We should do a podcast on AI.
We should do[inaudible] machinelearning.
(44:40):
I'm learning, but I want tothank everybody.
Theresa, thank you so much forjoining.
I think that, I think we mighthave a, we have four mikes in
this little lounge or that wemight have a new regular here.
Podcast.
There you go.
Nicely done.
All right, so thanks everyonefor tuning in.
We'll be back.
I'm in two Fridays from now.
Same Bat time, same bat channel.
(45:01):
Uh, so log in, check us out.
Um, thanks everyone here forjoining us and having a bit of a
chat.
Uh, we're gonna go haveourselves a good weekend and, uh
, get ready for helping ourclients with all of their cloud
strategy needs next week andhelping them to understand what
we're talking about and helpingthem to understand, helping
ourselves understand a littlebit more for sure.
Spreading that knowledge, that'swhat we're all about.
(45:23):
Learning too much.
You can never learn too much,too much.
So thank you all for your time.
Uh, have yourselves a greatFriday.
If it happens to be Friday foryou, if non us have a great rest
of your day, whatever day it is,uh, we're going to go have a
good weekend.
Uh, and cheers everybody.
Cheers.
Bye.
Speaker 1 (45:44):
[inaudible]
Speaker 2 (45:45):
shouldn't we play the music for us when we started to
get into that?
I can do that.
No, no, I'm just kidding.
Great.
Mark tried to throw you off.
Oh, good.
Let me, don't worry guys.
I got this.
Yep.
Whose club sodas that[inaudible].
Okay.
We need a safe word.
(46:06):
We need a safe word.
[inaudible].
Speaker 2 (00:03):
Hi, and welcome to Azure for the and mostly as your
focus cloud[inaudible] by theblue silver shift.
Really sit back and cry the loxgroup.
You talk to the cloud.
Speaker 1 (00:19):
[inaudible]
Speaker 2 (00:19):
all right.
Hello everyone.
One happy Friday.
Welcome to another Azure for the[inaudible] podcast.
We have your blue silver shiftcrew here, uh, with a new
addition actually to our podcasttoday.
So, uh, we'll just mention whowe got in the room.
So peg is dropping Mike's.
It's all good.
We're gonna go, we're gonna rollwith it.
Um, so you have your moderatorhere, Mark Wilson.
(00:40):
Uh, we have the partners, Craigslack and John Dobson.
When you guys go hook, go aheadand say hello and go ahead.
Hello.
That was me too.
I'm Craig.
Okay, there we go.
We also have Teresa Starskyjoining us.
I feel like I did.
I got it right, right.
Thank you Terry.
I appreciate the, the, um, theaffirmation.
Okay.
(01:00):
It's delight.
Teresa's actually, uh, somewhatnew at blue silver shift.
She's been, uh, she's joined thecompany for about six weeks now,
and she's gonna just be servinga very, very important purpose
for today.
Um, and she's going to be comoderating and I'll get into the
reason why.
So today's topic and yes, we aregonna talk in a second about
what we have for drinks, uh,before we get into the main
(01:21):
topic.
But, um, so today's topic iscloud terminology.
So there's, when, anytime you'retalking about cloud, especially
in some of our previous episodesof our podcast, there's a lot of
acronyms as a lot of terms thatpeople don't really don't really
know if you haven't been in theindustry for a while.
I know it's taken me quiteawhile to get up to speed on a
lot of these things and there'sstill quite a few that I'm fuzzy
on and, and we get asked all thetime by our clients and other
(01:44):
companies we're speaking to andwhat are these terms?
And so a lot of times peopledon't want to ask.
And uh, you know, we thought itwould provide a handy and handy
way to, uh, you know, just kindof lately a lot in the milk.
This is likely gonna be a twoparter.
So this will be part of one ofthe terms, uh, podcasts we'll do
into part two at some point inthe future.
No pressure.
So Mara, he is just so many ofthem possibly, you know,
(02:07):
everything's changed so fast.
Um, we'll look into the commentssection after the podcast airs
and see if people will ask theperson[inaudible] that'll be a
big list.
Um, so one of the things thatthat Teresa's going to do to
help us out here is if we haveanyone, and I'm looking at John
Right now, uh, that's answeringquestions and there's acronyms
in the question.
Our terms in acronyms in theanswer, in terms of, in the
(02:29):
answer that we might notunderstand.
Theresa's gonna jump in,interrupt them and say, Hey,
what does that mean exactly?
So before we get started, um, asper our usual tradition says
that since it is a Fridayafternoon for us recording this,
uh, we're hanging out in ourlounge in the office and, and
having a couple drinks.
Uh, I myself have a nude vodkasoda nude as the name of the
(02:53):
brand.
Um, and uh, I would have thelabor, it's a, it's a cucumber
mint John to you.
Very manly is a hundred ahundred calories per per cans.
Oh, it made it, that makes iteven more manly there.
Your current concerned aboutyour calories now truck and not
Speaker 3 (03:09):
that's so openly, but it's crazy.
Speaker 2 (03:11):
I appreciate it.
I'm very comfortable.
Um, and then, alright, soTheresa, what do you, what do
you got going on?
Speaker 3 (03:15):
I got a nice glass of old brode red wine from all over
the seat.
Speaker 2 (03:21):
Nice.
John, I think we have theoriginal or the standard for
you.
Standard riot and ginger.
All Ryan can do at homedeviation a little bit.
And then big change for Craig.
Craig, what do you have goingon?
Speaker 4 (03:33):
Well, anybody who's listened to more than one of our
podcasts knows that I like myscotch and my whiskey.
Um, so today I've got a niceCanadian whiskey, but I've added
some coke, zero to it.
Speaker 2 (03:45):
So sort of the words coke and whiskey.
Um, Craig's always got thereally Nice Scotch, but, and I
will say I'm a bit this way.
Nobody commented on my, uh, my,my Microsoft cloud, Microsoft
Canada cloud socks that I'mwearing today.
[inaudible] we have half thecompany where Microsoft saw
Tate's called Microsoft socks tothe awesome.
(04:06):
I was going to say something Iforgot to, sorry.
Speaker 3 (04:08):
Oh, it seems that this company is not jumping on
the short pants, then buy again.
And I'm okay with that.
Speaker 2 (04:16):
Oh, short pants for me.
Um, there we go.
Anyways.
All right.
Diverse.
I just like, as I was sittinghere, I noticed, I'm like,
nobody's commented.
Um, all right, well let's, uh,let's jump in.
Okay.
So today we're talking about, Ohyes.
Cheers everybody.
Thank you, Teresa.
Cheers.
Speaker 3 (04:36):
Okay.
So,
Speaker 2 (04:39):
um, our first question, I'm going to kind of
put this into two spots and I'mgonna Throw, I'm going to ask
Jon and correct to kind of, uh,double taking this answer.
So,
Speaker 4 (04:50):
okay,
Speaker 2 (04:51):
I'm start basic.
What is the cloud?
Before, before you startedanswering that, what is the
cloud and then what are thetypes of clouds?
So here I have private, public,hybrid, multi.
So what is a cloud and what doesit different through private,
public, hybrid and voltage.
Speaker 4 (05:07):
Oh, you're handing it to me, Craig, over to you to
start.
Well, what is the cloud?
That's a pretty broad questionbecause it means so many things
to so many people.
Speaker 2 (05:15):
Well it's got like a ten second shot, like a elevator
pitch.
Okay.
Some people think it's theiridea,
Speaker 4 (05:19):
iCloud, Dropbox, whatever.
But the cloud essentially camefrom the term where you know, an
old architecture diagram aspeople that represent the, the
Internet as a cloud on anarchitecture diagram.
So you're communicating trafficout to the Internet.
It would just go to this bigcloud on the diagram.
So that's as far as Iunderstand.
(05:41):
That's where the term, how itcame from.
So it really means anythingthat's stored or processed or
running up in the Internet.
And my definition to it is justan add on would be that, um,
I've heard it referred to assomeone else's computer, uh,
your computer
Speaker 3 (05:58):
at someone else's.
And on the far end, Eric Schmidtfrom the Google, I don't know if
he's still the CEO of Google ornot, but he was the guy that
actually coined the term 15years ago.
Oh Wow.
Yeah.
So it is, as Craig said, it'snot here.
Speaker 4 (06:15):
Okay.
And then the different types ofcloud.
So yeah.
So different types of clouds.
The second part of yourquestion.
So I'll kinda just rattle themoff.
And John said that there's more,so he'll probably add more.
Right.
So private cloud is a conceptthat's been around for decades
where it's, I'm gonna use aterminology that's very it
(06:35):
centric, but it's co-location.
So it means, uh, in the, youknow, many years ago you would
buy a server, you maybe didn'thave a data center that, uh, you
were hosting yourself on site.
You would buy that server, putit in somebody else's data
center and they would eithermanage it for you or you would
manage it remotely, whatever.
(06:56):
So that has been rebranded asprivate cloud.
So it's because it's either yourequipment or it could be still
somebody else's equipment asJohn just said.
Um, but it's in a data centerthat's not yours, but it's not,
uh, it's like a privateconnection, private, everything
like that.
Speaker 3 (07:13):
It could be their on premises data closet.
Yes.
And I think that they do itbecause they feel jealous of the
big guys having a big cloudpublic cloud.
And then therefore they say, oh,I've got a private cloud.
Where do you keep that privatecloud?
Speaker 4 (07:27):
Yeah.
And they can call it that onlybecause it's connected over an
internet connection or some sortof network connection is not
directly located on site withtheir, where their main
operation is.
Um, public cloud is whateverybody would refer to as, you
know, the Google, the Amazon andthe Microsoft Azure, um,
(07:48):
environments.
Those are public cloudenvironments that are open to
all, they've got essentiallyinfinite scalability, uh, many
geographic locations, uh, aroundthe world.
Uh, and they, they provide manymore services than a private
cloud can.
I don't know if you want to addhere.
Speaker 3 (08:06):
Yeah.
And just to go back a bit on, uh, private cloud, um, technically
for what Facebook has and what,um, Google has are private
because they're for theirinternal systems cause they're
massive computing areas.
Yup.
And they're their own things.
They're not open to the publicand that's a good point.
Yeah.
(08:26):
And they're also private is likewe said, closets and someone
else's computer.
Hybrid.
You want to take this one, John?
Uh, I have a mixture of, um, onpremises systems, uh, and um,
meaning I've still have my oldmindset, my old computing style.
(08:48):
And I've also started my cloudjourney, some things in Azure.
So I've been a mixture ofenvironments, probably
Speaker 4 (08:55):
very common.
Oh, that's more common than not.
Absolutely.
Yeah.
There's very few pure cloud,whether that's private or public
cloud companies.
Correct.
Speaker 3 (09:06):
Correct.
Multi, multi cloud.
Uh, is that more of a strategythan anything else?
Because a, I have found acompany, um, one of the design
principles that an architectaspires to is to have, uh, I
want the data in all threeclouds.
And one of the reasons is theydon't want to be held ransom by
any of the three public cloudproviders, the big ones.
(09:28):
So they try to, um, have astrategy of multi-cloud for
their service offerings at it's,um, got diminishing returns
though.
Was that because, um, the effortyou put in to designing and
getting a solution like thatworking and it only really works
for specific use cases.
(09:48):
If, for example, the padsofferings on all these clouds
cracked your couch are veryspecific to the cloud and you
would have to have differentpaths, solutions in place per
public cloud that you're using.
So we often hear customerssaying, oh, we're going to have
a public cloud, we're going tohave a, um, a multicloud
(10:10):
strategy and it's just y and[inaudible].
I mean, it's the diminishingreturns component of it.
Yeah, correct.
So if you're a business payingfor that, you, you'd be
questioning why someone wouldactually be doing that.
So I'll just give you anexample.
Uh, Netflix, uh, has an Uber andthose guys have an Amazon
(10:34):
component and they would havemulti regions within the same
provider, but they wouldn't haveit off into a different public
cloud because it's just toohard.
There's enough fail overcapability and redundancy inside
of public cloud offerings thatyou don't have to go that way.
So a traditional business wouldreally never have to go
multi-cloud ever if you're beingtold that you should question
(10:58):
it.
Speaker 4 (10:59):
Sony announced recently that they're, so if you
will, if you wanted to like areally good example, um, in the
future to give, like you justgave for Netflix or Uber using,
uh, AWS as you can say, Sony isgoing to run all of their online
gaming on Azure.
Yeah.
So that's, that was a huge, hugething to make Azure much more of
a household name in the verynear future.
(11:19):
Yeah.
This doesn't actually definemulticloud, but there's, just to
touch on it briefly, there's anumber of hidden costs that come
with going multi-cloud.
So if you've got, you know,services that are talking to
each other, but they're goingfrom Google to Amazon to Azure
or just the two of them you'regoing to have.
And this is going to be anotherterm that we'll define later.
(11:40):
Egress network charges.
Yes.
So we'll define that in aminute, but uh, that you're,
you're, you've got networkbandwidth charges, but then
you're also on top of that,you've got management costs.
So your, your team now has tohave multiple, um, skill sets to
manage different clouds causethey're all managed different
ways.
There's no single.
(12:01):
Yeah.
Speaker 3 (12:01):
And Are you, are they all active, active, active, or
are they active?
Passive.
Passive.
Oh No, that's the case.
Well, meaning that one is liveand the other two are sitting
dormant.
Yeah.
But you got to pay for them.
Speaker 4 (12:11):
Yeah, that's true.
So there's the diminishingreturns again.
Yep.
And actually something Johnmentioned too, about it being a
strategy.
Multicloud is a strategy becausecompanies don't want to be held
captive by any one vendor.
If, you know, Microsoft orGoogle or whoever decides to
dramatically change theirservice offering or their prices
(12:32):
or whatever, which it'd be hardfor them to do because it's a
very competitive market.
So they're all basically quitematte price matching.
But, um, it's, it brings upanother term that I wanted to
quickly define cause it'srelevant here as vendor lock in.
So, um, it's very relevant inthe cloud.
It's also relevant in, you know,on Prem.
(12:52):
But, um, the, the concern ofbeing locked into a vendor and
you, you don't have a choice to,to ever move off without
reinvesting and re redevelopingor redesigning your entire
system.
So that's why they're kind ofadopting a multicloud strategy
so that they can just at a flipof a switch, in theory, switched
(13:13):
to another cloud vendor withoutany implications.
W uh, design criteria becauseyeah,
Speaker 3 (13:19):
they lock in on ERP, CRM, uh, where they're working,
who their partners are in life,everything.
We create the acronyms toshorten the conversation.
That's why that's the purpose ofthis to, okay.
Speaker 2 (13:38):
I think we spent enough time on it as well.
We're going to jump into thenext one, so I know I'm not the
moderator.
Next question.
Ah, you know, there's a lot ofdifferent permutations of the
cloud and I feel like I've got apretty good handle on, on these.
I feel like if you asked me, Icould keep the definition, but
I'm going to, uh, I'm gonnathrow it over to you guys.
Oh, you should try it.
I want to hear yours becauseeverybody has different
interpretations, so.
(13:59):
Alright.
Alright.
So, um, we have a, so here'ssome really common ones that
have been coming out.
So we have[inaudible] pads, SASdrives as, as, so I think it's
as, I think it's how youpronounce it.
So these are all acronyms.
Uh, so IAAS is I a s.
Um, so that's, uh,infrastructure as a service.
(14:20):
Sorry.
Um, we have, uh, paths, platformas a service.
We have saas software as aservice.
Yes, you can.
Yeah.
So I'm going to go back throughthe mall.
Um, uh, so drowsy is actually abelieved disaster recovery as a
service.
Yeah.
Okay.
Uh, and then Zass I think it'sjust anything as a service as x,
a s yeah.
(14:40):
X, XP, anything.
Yeah.
Okay.
So I'm actually going to startwith Sass cause I think that
that's what people have the mostexposure to.
Everyone needs a little sass intheir life.
Everyone has a lot of Saas inlife.
Actually, I probably ain't evenlisten to this as well.
Speaker 3 (14:55):
Nice.
Speaker 2 (14:56):
Um, so SAS software as a service is really any kind
of, uh, any application thatyou're accessing through a web
browser.
So if you are logging into anaccounting software through your
web browser, that's going to bea software as a service.
Uh, you could, you could even,you could consider, um,
something like Spotify.
Uh, you know, and so that's aconsumer based SAS product.
(15:18):
Um, you know, real.
So yeah.
So anything that that is, thatis access through a web browser
is going to be
Speaker 3 (15:24):
all those office three 65 as a SAS product office
three 65.
I'm going to get into somethings that are listed in here
that people think are pads, butI actually consider them to be
SAS.
Speaker 2 (15:34):
That's actually, so that was going to be one of my
questions.
I'm interested in that.
Um, so, so SAS is really, mostof the time when you're, most
people's experience with thecloud is going to be through SAS
I believe.
Um, I as so infrastructures as aservice.
That's really when your ma, Imean for our purposes and from
my experience, it's mainly beenwhen you're putting an
application onto the cloud, butyou're not really, uh,
(15:58):
refactoring the application,you're really just re hosting
the application.
So a lot of the times we'redoing a lift and shift of an
application that may be sittingon an on premises server.
So you have some sort ofaccounting software, um, or
something that's sitting inside,uh, sitting on your server
inside of your office or insideof a private cloud.
Um, and it, you know, might besitting on a, you know, your
(16:19):
windows server or your SQLserver and you would then lift
that application up, shiftedover the cloud, drop it down.
And that is infrastructure as aservice.
So you're not really changingthe application at all.
Um, you're really just rehosting it.
Yeah.
Um, same old, same old, sameold, same old.
(16:39):
So you're just doing the samethings a lot.
You know, I've actually spokento a lot of companies, well not
really, not a lot, but a fewthat have said to me, you know,
we have this application, it'ssitting on our server.
We can't put it in the cloudbecause it won't work in the
cloud a lot of the time.
I think that's because they'reexpecting to have to redevelop
it into, uh, into, uh, a SASproduct or, or put it on a, on a
pass, um, uh, infrastructurewhen really we can have any
(17:02):
working exactly the same way asit does now.
Just living in the cloud.
Uh, PAS is a platform as aservice.
So really that's when you'relooking at rebuilding the
application.
If you're looking at migrating,um, you know, you're rebuilding
the application in the cloud.
So, um, this is where I start toget a little bit, uh, I know
(17:22):
that there's a lot ofimplications to pass and it's a
much, much more powerful.
Speaker 3 (17:26):
So if you had sequel in an I s so I for structure as
a service.
So you had a VM with sequel init and then you moved it to a
pass sequel.
You don't actually get the Oos,you just get the SQL service,
but you manage it through thesame type of console.
I'm just making imaginary marksin the air.
So we've got sequel, which is atype of database VM in their
(17:49):
virtual machine.
We had o s operating system andI'm like, what's wrong with me?
I have the question because markjust said, you know, sometimes
you can just lift and shift todo the[inaudible] approach, but
there are obviously times whereyou do have to rebuild an
application to the past.
I don't know if calling it anapproach is the correct thing,
(18:09):
but what's the deciding factorbetween whether you can do an I
as versus having to do a passbecause I assume rebuilding is a
lot more costly and people wouldbe probably tending to want to
do an I as for cost purposes atleast.
So how much money you want tospend to do the refactoring,
whether or not that's going toactually give your business
significant value in doing it.
(18:31):
And because, because when youmove to pass, it's going to
reduce your infrastructure andincrease your ability to scale.
So if you've got a businessthat's going to scale up, it
would be, uh, advantageous totake an Ios SQL server, which is
a traditional server and convertit to pass because then you'll
scale it up and you'll get muchbetter value overall out of it.
(18:51):
So it takes some longterm viewto make a, yeah.
Worthwhile.
Correct.
It doesn't often, it's anevolution to, it's a journey.
Some, some applications can't doit because they're too old.
They just will not talk tosequel or talk to.
And it's not worth theinvestment.
That's where the word legacycomes in.
That's another[inaudible].
Speaker 2 (19:14):
Alright.
Um, so we have drowsy, sodisaster recovery as a service.
And I mean, I think that'sreally just looking at, you
know, making sure that you haveyour disaster recovery
infrastructure, um, set up andestablished so that when, if you
know, if an event is occurring,then you have a mix of, I'm
assuming automated processesthat are going to um, you know,
(19:35):
spin up your infrastructure andanother, uh, Geo low, uh, you
know, geographical location.
Um, but then also when you'relooking at the, at the service
of disaster recovery, it'sreally, um, new, constantly
monitoring, updating, adaptingto new challenges, making sure
that you have, um, you know, anykind of, uh, changes to your
systems or processes that arebeing required, uh, for, you
(19:58):
know, new infrastructure changeswithin the cloud hosting
provider or within yourapplications that everything is,
you know, set up and ready sothat if something should occur,
you are ready to take advantageof those disasters.
Speaker 3 (20:08):
It's recovery service .
And while a disaster recovery asa service works with on premises
applications to the cloud andfrom the cloud to the cloud to a
different cloud, to a differentregion within the same provider,
um, it never really got, um, anya strength behind it or was
popular as a service untilpublic clouds came about.
(20:28):
So in other words, it is a typeof service that was often, uh,
preconfigured locally a onpremises in a private data
center.
And then that's it.
It's, it just took off with, um,public cloud and Dra as a
service is almost, you can alsodo it as like an outsourced
service that you're subscribingto
Speaker 4 (20:48):
in a company, takes care of your entire Dr Strategy,
your actual backup and therecoverability and doing all the
testing of your environment.
Speaker 3 (20:57):
So, which is something we do.
Is there a disaster scenariowhere it would not be
recoverable?
For instance, we talk about thecloud, but everything still
comes down to a machinesomewhere, right?
That would not be recoverable.
Is there an instance or was thata quite extreme situation?
Yeah.
So I think when you're sayingeverything comes down to a
(21:19):
machine is the end user that'susing that system rather where
it's stored.
So you can architect it so thatit can wish them, you know, all
of North America drops off themap.
You can still keep on running,right?
So you could, you couldarchitect to that level, but it
depends on the kind of business.
So if you have a hightransactional business where
(21:39):
people are processing creditcards all the time, and if that
system's not down, they can'tcomplete the sale.
The customer walks out the door.
You have to design a solutionfor that to be able to do it.
So that's not disaster recovery.
That's high availability,business continuity, business
continuity, high visibilitywhere you're going to actually
have these systems working,active, active as partners in
(22:01):
different physical regions withredundant connections,
crossovers, whatever you want tocall them, into that same
system.
Right?
I can't go here.
I'll go there.
And, and companies know it'sworthwhile to pay for that
because they know the cost ofdowntime.
They know that at say, um, theapple store, if they can't
process sales each, each applestore makes$50 million a
quarter.
(22:21):
A quarter.
Yeah.
And um, so if you had a businessthat made$50 million a quarter,
you would want a redundant,you'd always want that cash
register rotting.
Probably very small cost.
Oh, compared to there.
But sometimes it's not a smallcost compared to what it is.
So it's not, it doesn't makesense to do it.
The corner store actually inpackets of bubble gum,
Speaker 4 (22:42):
I was just going to make that exact example because
the other day I went to thecorner store just down the
street and their Internetconnection was down and he was
turning away business because hecouldn't process credit cards
cause all of his credit cardstuff goes out over the
internet.
And he was visibly frustrated as[inaudible]
Speaker 3 (22:59):
people were leaving.
He's like, I'm losing, I lookedinto his phone.
I don't know cause that's[inaudible]
Speaker 4 (23:04):
if you look at the costs and say another Internet
connection, 50 or a hundredbucks a month, um, if he lost$50
in that one day, he would havepaid for that back, that
redundancy there.
So I think it applies to everybusiness.
They just have to run their owncost benefit analysis to figure
out does it make sense for thatcost?
Cause it doesn't, it's in somecases it can be very expensive
(23:25):
to get that fully redundantsolution.
And if, if the downtime doesn'tequate to the dollars, it's
gonna cost to, um, to put thatredundancy in.
Then many companies will justaccept that risk that yeah.
Speaker 3 (23:39):
And they'll have a recovery time objective.
That's another definition youdidn't use it.
And in RPO is recovery pointobjective.
So again, when you've got atransactional system, that means
it's journaling as opposed to apoint in time.
Just bring me back to lastTuesday.
Just bring me back to last nightat seven o'clock.
Most businesses are covered bythat type of recovery point
(24:02):
objective.
But then they also have arecovery time objective that
says, bring me back to thisparticular time.
It sounds like we're at the[inaudible].
Speaker 2 (24:12):
I don't know if our bikes aren't getting out, but
there's a seagull on our balconytoday, but the seagull is
tripping out.
I should've actually mentionedwhen we started, I, I didn't,
but uh, Theresa is, um, actuallythe person who is has to listen
to us later and go through the,go through the podcast and pull
(24:34):
out some of the contents.
So a, she actually helped toinspire this a, this particular
topic and um, Yup, I will saythat sometimes some of the auto
transcription, so some of thepodcasts that she's pointed out
to me have been pretty funny.
Um, I think that's thetechnology that still catch it
up.
(24:55):
Um, all right, last one on this.
This specific topic is as oranything as a service and I w I
mean it's, well, yeah, cause DRMas a service
Speaker 3 (25:04):
doesn't really fall into the same categories as I,
as pals and SAS.
So it's really, I call it excess, but you know, as sounds
cooler, I think so.
I don't know how the properpronunciation of it, but it's x
meaning anything as a service.
So I think in the future as weget more and more things served
(25:24):
up from the cloud, they are as aservice.
Right?
So it's, yeah, we offergovernance as a service.
There you go.
Yup.
Temple.
And I'm sure this time nextmonth or two months from now,
we'll have another service inthere that we're streamlining
pricing as a service.
Why don't we define what cloudgovernance is while we're,
(25:46):
you're going to talk about itlater.
Okay.
No, no, that's, that's onto theother list.
Okay.
Speaker 2 (25:50):
Well, I mean, so we're going to, I mean, we're
going to read up into, we'regoing to jump into a lightning
round while we want.
Oh yeah.
Okay.
Well let's hold off on that fora second.
Cause it's actually the toptalks.
It's actually the top of thelist in the lightning round.
Okay.
So we're going into a lightninground now.
So we're going to talk about,I'm just gonna throw some terms
and I'm going to point jeopardymusic.
(26:10):
Okay.
And you guys, how long do wehave?
You have like 15 seconds.
15.
That's easy.
No, no, no, no.
Per, per, per, per term.
Okay.
15 seconds per term.
Cause that'd be cause you knowhow many things you could rattle
off.
Actually that's what you shoulddo.
I had the bonus.
We should actually have acountdown timer.
If 30 Sab left the most amountof acronyms.
Now you can define it.
(26:31):
Correct.
Anyway, sorry.
We should actually be inseparate booth.
I mean I may be able to, to comeup with something like that
right now on the fly too.
We do have a long list here likewe got, um, hey, it doesn't
matter.
Okay.
Okay.
So let's, let's take a look.
We're going to jump into relatearound a little bit different
than the normal lightning round.
You got, uh, 15 seconds.
I'm might give you 20 if youreally need it.
(26:51):
But we're going to start offwith cloud governance and we're
going to go to John.
Go ahead.
Speaker 3 (26:56):
Cloud governance.
Well, how we govern our lives ishow you govern your cloud.
Meaning, uh, you use a solutionevery day that says, I'm not
going to spend this much money.
I'm not going to do these thingscause they're harmful to me.
I'm not going to do somethingelse.
You have a governance system andcloud governance is your ability
to manage and the policies andprocedures that you're going to
(27:17):
use to actually do it.
Speaker 2 (27:19):
It's good enough in 15 seconds.
That's a good analogy.
You mean it was about, it wasunder 2016 five given the
buzzer.
I do the same one or no, don'tworry.
Maybe a new one.
So, uh, Craig Cloud CustodianCloud Custodian
Speaker 4 (27:33):
is somebody who's monitoring and maintaining the,
enforcing the governance, makingsure that those policies that
John mentioned are adhered to.
Tracking the cost, uh, lookingfor workload optimization
opportunities and so on.
Speaker 2 (27:48):
Yeah.
Pretty good.
15 on the dot.
Oh my God.
He didn't eat by 1.5 second.
Alright, John,
Speaker 4 (27:55):
for all
Speaker 3 (27:56):
sprawl is a, when you don't have cloud governance,
your systems will go out ofcontrol like weeds in a garden.
Speaker 4 (28:03):
Okay,
Speaker 2 (28:04):
awesome.
Under eight seconds.
You don't, you don't, you don'thave to just under 20 seconds
wind up, be informative as well.
Needs to be as quick aspossible.
Um, clouds
Speaker 4 (28:16):
brawl is really related to like people just not
controlling the amount ofresources they're spinning up
and not deleting them.
Yeah.
Speaker 3 (28:26):
When you say that you mean things like people creating
files and putting them somewhereand forgetting about them
Speaker 2 (28:31):
that files is an example.
But we'll do that with systems.
Speaker 4 (28:36):
Virtual machines, servers, networks, just
resources a yeah, if you don'thave a process in place to
contain that sprawl occurs, thesystems, et cetera.
Okay.
Speaker 2 (28:46):
Okay.
So next is um, I'm going to giveyou the term, but I'm going to
pair it with another term thatis used often.
I've used it in the past.
It's incorrect.
Um, so on premises definitedefinition and why is on
premise, why does that make nosense?
And who went last, correct.
You went last?
No, no, John, this is one of mypet peeves.
(29:08):
I should probably get to thiswith Greg.
You're up.
Speaker 4 (29:11):
And, and people who worked with me long enough know
that I pointed out all the timebecause it's a common mistake
and it so on prem or onpremises.
Um, the proper terminology iswhen you've got servers or
equipment located at your mainprimary res, uh, not residents,
primary office of operation andit's running the systems there.
(29:34):
I know I'm going over 15seconds.
That's okay.
On premise, which is, well justthe word premise is like the
premise
Speaker 2 (29:41):
of doing something right.
That's, yeah.
Yes.
So on premise it doesn't,doesn't make any, it's not
proper English.
So, and now that I'm an Englishmajor, that'll be more
Speaker 3 (29:53):
or English Nazi, my boss just went up and my
estimation over that.
Wow.
I must have been pretty low tobegin with.
I was an English major too.
Well I wasn't, no, he said hewasn't[inaudible] that's why I
was pointing at, you know,discovering the distinction.
(30:14):
So oftentimes in our industrypeople say, Oh, and then you
just move your stuff from youron premise stuff up to the
cloud.
And I keep thinking to myself,so right now, and I'm going to
try and boil it down even betterthan Craig did.
We are sitting in a premise.
Yes.
And for me to say the sky isblue is a premise.
That's a good way to put it.
So the point is is that peopleget that wrong.
(30:36):
And,
Speaker 2 (30:37):
and I have an admission to make, I'd say
probably until about five yearsago, I was saying on premise all
the time too.
So I've said many times there'sno shame to be
Speaker 3 (30:48):
but, and, and, but the real question is, do you
correct some of them?
No.
Could you say that?
Do you correct your customer?
Could you say that?
Oh yeah, yeah, you do.
But do you correct a customer?
Would you correct a customer orwith the customers?
Speaker 2 (31:07):
You don't want to hold, you don't want them to,
you don't be saying the wrongthing to other people, right.
It's like when somebody hassomething stuck in their teeth,
you want to help them out.
So, so could you, could you say,could you say that like the
premises is the lounge in ouroffice and the premise is that
we're here to do the podcast?
Yes.
That's okay.
Summation.
All right.
Um, so John, a new throw, atricky one over to you.
And I'm skipping, I'm skippingsome here cause I wanna this is
(31:30):
like tricky ones.
I want, I want to know, oh, whatis machine learning?
What is AI?
Oh my roots between she, betweenmachine learning and AI.
And I'm not going, I'm notgiving you a 15 second timer.
Okay.
So this one we're both, I'm
Speaker 3 (31:47):
going to answer in on.
So here we go.
Artificial intelligence is ascience fiction, uh, concept.
So skynet as an example, uh,that's where it comes from
originally.
Meaning that you can actuallygive the computer, it's an
entire domain and machinelearning is part of that domain.
(32:08):
And I think deep learning is theother part of that domain.
They call it deep learning.
Um, and so AI is where, um, youare going to have your data
centrally stored and hopefullyin the cloud.
And that should be your firststep on your digital
transformation.
I don't know if that's a wordthat we gotta define.
Probably the second thing is, isyou, once you're at that spot,
(32:30):
meaning your data's not all overthe place or unknown to you, you
actually can apply certainpublic cloud AI concepts like
machine learning into a, to lookat your data.
And what it's gonna do is it'sgonna find outliers.
It's gonna find it's gonna,you're gonna work with it.
So a layman can work with it.
You don't have to be a datascientist to actually determine
(32:52):
where, uh, to train the data.
Meaning look at this.
Y'all train the algorithm and tomodel the data better so that
you can actually ask itquestions on insights like, Uhm,
questions, any, any type ofquestion.
All like, instead of having anaccountant look at all your data
and say, hey, you got to raisethe price here and drop it
(33:14):
there.
You can actually have an AI thatnever calls in sick, that is
continually looking at your dataor a machine learning algorithm
that is essentially pays you gotell you the exact same good
advice, but probably on a muchgreater scale if you have a huge
volume of data and the largerthe volume sets of data, the
(33:38):
better.
And um, here's the reality.
That type of technology is gonnamake humans live to be 200 years
old because that kind oftechnology is going to look at
your data as Apple's collectingit as Google's collecting it and
whatever.
And it is gonna say, Hey, thistype of activity or whatever
you're doing needs to stop orincrease or otherwise, or you
(34:00):
need to go for an exam onsomething so that, that's where
it's manifesting itself rightnow.
So we wouldn't be able to getthere with our biological
brains, believe it or not.
But AI can actually push youthere if it can do that for the
human body to 200 years,essentially double plus the
lifespan.
Imagine what it can do for yourbusiness.
Speaker 4 (34:22):
I don't know if I can even add to that cause that
that's, that is a greatdefinition.
I just read, I I, I agree thatml or machine learning is a
component of artificialintelligence because artificial
intelligence spans manydifferent spaces.
It can looting science fiction.
Yeah.
And so if we talk about bots forexample, where you know chat
(34:46):
bots, which is probablysomething you need to define as
well, but it's, you could be ona website and you get a little
pop up window that says, Hey ourcustomer service people are here
to help.
And you're interacting with avirtual customer service agent
that does not even potentiallymost likely a real person.
Um, cause they've built thesecommon things.
(35:07):
It's using artificialintelligence, not necessarily
machine learning.
They know that if a certainquestion or, or it can figure
out that it's relating to asimilar question, it's in their
database, it will spit out acertain answer.
Right.
So that's what I would define aspart of artificial intelligence.
Machine learning is, is thewhole data side.
And, and being able to get it toa point where you can create an
(35:31):
API that is, and by a machinelearning algorithm you send it a
single data point, um, and itwill be able to predict
Speaker 3 (35:38):
something on the other end of it.
So that is, yeah, and businesseswant the predictability.
So again, if you think aboutbusinesses from 20 years ago,
they were in beyond, they werelike driving a car at night with
the lights.
They were, okay, this justhappened, let's turn.
Okay, let's do this.
And AI going to provide thatpredictability moving forward so
(36:01):
that they've got room to grow,ability to plan, et cetera.
So good, good summation.
Speaker 2 (36:08):
One of the ways that I have been kind of as I'm been
trying to wrap my mind aroundthe differences, and I'm going
to use, you mentioned likeliving to 200 so I'm going to
piggyback on that as like a, onthe health thing, the health
idea.
So the way that I've beenlooking at machine learning, and
I think that I think they'retalking about machine learning
as a component of AI is kindawill help a lot of people.
(36:28):
Um, I know that it definitelyhelps me.
I've always been looking at itor recently I've been looking at
it as like, uh, so, okay, so onthe health thing, like medical
imaging, so you program amachine learning system to look
at the, you know, a brain scanof 10 million people or whatever
and you say, um, every like lookat at the brains of 10 million
(36:52):
or have x amount of people whohave had a stroke and identify
at, identify a commoncharacteristics of those brain
scans and then spit outsomething at the end that's
going to tell you, yeah, this isa common thing that'll, that'll
cost Australia works.
Yeah.
Whereas AI, I've been kind oflooking at it as like you would
build a system where you give ita a same amount of brain scans
(37:15):
and it decides for itself whatis causing what and what is, you
know what it's, cause it's our,it's an artificial intelligence
is learning off of itself.
So it's not necessarily, you'renot telling it.
I identify these characteristicswith the in this magic to this
result.
You're saying look at thesescans and then seeing what the
heck comes out the other.
Speaker 3 (37:35):
It's bringing up parallel processing.
So again, it's to go on yourpoint there.
I was thinking about it asyou're saying it, we, it wasn't
available before public cloud.
So same way as a service wasn'tavailable.
AI Really wasn't available.
You could go and buy IBM Watson,but then you've got to have a
(37:56):
data center, a private cloudsync the cost into it and then
throw your data at it.
It's very specific, exactly howyou do it.
And even the amount of data,something like that could look
at was was limited by yourcapacity AI in the cloud.
The more data I have, the morecan look at it all at once.
So the parallel processingcapabilities are huge.
(38:18):
I have another, uh, um, call itan analogy or otherwise where AI
traditionally and historically Iguess has been more a lot of if
then else statements.
So think of it, you're thinkingof a autonomous car, right?
A self driving car.
Um,
Speaker 4 (38:36):
you know, it's a car driving straight down the road.
It needs to make millions ofdecisions at a time.
Okay, so simple is the redtraffic light red stop.
So if the traffic light appears,red must come to a stop at the
line.
If the traffic lights screen,keep going.
So keep it simple with that.
Whereas with, when you add inthe machine learning element,
(39:00):
you have now all theseautonomous cars, millions of
them around the world feedingtheir data to the cloud because
the cloud has infinite capacity.
That data can now be analyzedand those, um, if then l
statements can be, uh, um,manipulated and changed over
time to evolve, to become moreefficient and more intelligent.
(39:23):
So now let's just take anothersimple example.
So if the lights red stop, butnow because of say a crash
happened, that it ran over apedestrian cause somebody who
was crossing the light on a red,the light was green, the car
started going, but theautonomous car drove over
somebody.
Now that machine learning, thatAI model can add in if the light
(39:46):
turns green go.
But Oh, if there's a personstanding in front of you, don't
go.
So it's learned that Ms.
Dot.
AI Model has now learned fromthe machine learning data and
the all the masses of data.
Speaker 3 (40:00):
So I don't want to take this too far away from, um,
definitions, definitions, butfor the casual listener taking
in these AI, uh, stories, is itliable to be a Frankenstein's
monster one day?
We're all a biological computer.
(40:22):
We are a biological computer,right?
Yeah.
Or, or a computer as a, uh,artificial, a facsimile of us,
correct.
Of our brain and the way ourbrain works and everything.
The, with what Craig justexplained, we do that.
Like, I'm doing that right nowand I'm thinking about other
things.
I'm thinking about what I'mgoing to say.
I'm, I'm breathing, I've got anautonomous nervous system that's
(40:44):
actually going and firing aswell all at once.
Computers.
So with what Craig started,we're in the early days of, but
the thing that computers havetaught us is that they can
accelerate and go up quitequickly.
So we've got self driving cars,we're going to have packages
delivered, you're going to ordersomething and something's going
to come in a in a drone and dropoff and it's going to be
(41:04):
reliable.
It's going to be a solidbusiness model.
We're probably 10 years awayfrom at least from that being
mainstream.
Like I mean more people drivingautonomous cars than not.
We have an employee here that'sgot an autonomous driving car as
well.
So it's already starting tohappen.
Um, I think skynet and theconcepts that are in popular
(41:26):
culture or, um, I'm not surewhat the, the alien or the AI
was called in 2001 but that oneis, well, they're, they're out
from that by quite a bit.
Meaning that I can actuallyponder while do I want to wear
different types of clothes orwhat do I want to do tomorrow?
A computer has no concept ofthat.
Speaker 2 (41:48):
So one of my favorites Gov of of quote
unquote evil AI because it can'treally be evil.
Um, oh this is my, this is myfavorite, is my favorite example
of, of a way that that AI wipesout humanity.
So, oh my gosh.
So hasn't got a conscience.
So there's, so there's, there'sa, there's a business and
(42:09):
they're an a and they're in thebusiness of handwritten letters
cause people were opening uphandwritten letters more than
the open up something that isclearly printed.
So they actually build themachine.
It's a very small machine and ithas a pen on it and you can, you
buy this machine, it'll handwrite your letters for you hand
write the envelope and he'dwrite the letter.
A person didn't write it, themachine wrote it.
Um, so they're trying to get itto that.
It looks better.
(42:29):
So they're trying to get morerealistic handwriting out of
this thing.
So they connected to theInternet.
And they tell it, learn how tohand write better.
So it, so it starts scouring allthese handwritten images and
learn, but all of a sudden forsome reason, for some special
case, this one instance of thishandwriting technology, this one
algorithm to look out there andlearn how to write handwriting
(42:49):
better.
That's the singularity andinspires conscious.
Then realizes, hey, if I breakout of my confines, I can hand
write better and it has onemission hand write better.
So it because it can learn sofast, it learns very, very short
amount of time how to developmuch, much smaller technology.
All of a sudden it's buildingnanobots and it realizes, hey,
(43:13):
humans are not actually nanobotsbeing like molecular, regular
local machines that can do thewriting itself.
Rarely humans are actually goingto stop me because they don't
want to die, but they're goingto stop my mission of writing
better notes.
So it, you know, basically wipesout.
It uses Naoto boss to wipe outthe whole planet and it uses
(43:35):
needle bops to reconfigure allthe matter to, to build these
little machines that itrealizes, hey, amount of matter.
So then built rockets to blastoff to the moon, back to Mars,
then to other solar systems thatthe entire galaxy and the entire
galaxy is covered in these tinylittle machines that are writing
the same letter over and overand over again.
Is that from something you getbetter at?
(43:56):
Right.
Actually be a black mirrorepisode.
Ah, yeah.
I'd read that as like a shortstory.
Oh yeah.
That's awesome.
Perfect example, because itdoesn't care.
There's no more reality.
It's not like good or evil orwhatever.
So skynet doing exoskeletonswith skin on it, writing better
letters.
Correct.
That's awesome.
That's my favorite AI story.
That's a good way to go.
(44:18):
Papered and beautifulhandwriting.
I guess there's worse ways.
I'm all right.
I think that we're about out oftime here, so we're going to cut
it off for round one.
We're going to do round twoagain.
I blamed myself a little bit forgoing off of my AI tangent.
Um, but we're going to do roundtwo.
We still call rise eyes.
Sorry.
We should do a podcast on AI.
We should do[inaudible] machinelearning.
(44:40):
I'm learning, but I want tothank everybody.
Theresa, thank you so much forjoining.
I think that, I think we mighthave a, we have four mikes in
this little lounge or that wemight have a new regular here.
Podcast.
There you go.
Nicely done.
All right, so thanks everyonefor tuning in.
We'll be back.
I'm in two Fridays from now.
Same Bat time, same bat channel.
(45:01):
Uh, so log in, check us out.
Um, thanks everyone here forjoining us and having a bit of a
chat.
Uh, we're gonna go haveourselves a good weekend and, uh
, get ready for helping ourclients with all of their cloud
strategy needs next week andhelping them to understand what
we're talking about and helpingthem to understand, helping
ourselves understand a littlebit more for sure.
Spreading that knowledge, that'swhat we're all about.
(45:23):
Learning too much.
You can never learn too much,too much.
So thank you all for your time.
Uh, have yourselves a greatFriday.
If it happens to be Friday foryou, if non us have a great rest
of your day, whatever day it is,uh, we're going to go have a
good weekend.
Uh, and cheers everybody.
Cheers.
Bye.
Speaker 1 (45:44):
[inaudible]
Speaker 2 (45:45):
shouldn't we play the music for us when we started to
get into that?
I can do that.
No, no, I'm just kidding.
Great.
Mark tried to throw you off.
Oh, good.
Let me, don't worry guys.
I got this.
Yep.
Whose club sodas that[inaudible].
Okay.
We need a safe word.
(46:06):
We need a safe word.
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