WEBVTT

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The information economy as a rod.
The world is teeming with innovation as new

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business models reinvent every every industry.
Inside Analysis is your source of information and

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insight about how to make the most
of this exciting new era. Learn more

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and inside Analysis dot Comside Analysis dot
com. And now here's your host through

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Eric Kavanaugh. Yes, all right, ladies and gentlemen, Hello, and

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welcome backwards again to the only coast
that ghost radio show in the US of

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A that's all about the information economy. It's Inside Analysis her. It's truly

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Eric Kavanaugh here, and boy am
I excited to dive into the world of

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continuous delivery CD. You may have
heard about ci CD, continuous integrations,

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continuous deployments, I should say,
And we have with us today an expert

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in that space, where we're talking
to Adam Frank from a company called Armory,

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all about continuous deployment, what that
means, why it matters, how

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it relates to get hub for example, and get lab and really how development

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is changing. You know, I'm
an old timer, so I remember the

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waterfall approach which took a long time, and it took in months to years

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to deploy something. And that model
is just broken these days, so you

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cannot compete on the stage, especially
in the cloud, with that kind of

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model anymore. You won't be able
to compete for very long. So things

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are changing, and Kubernetis is a
big part of that. All the observe

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ability vendors we've seen spring out of
that environment is a big part of that.

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I just actually read some slides for
a webinar will do tomorrow with merv

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Adrian, the IT analysts from Gardner. He's super, super smart guy.

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He had this line near the end
of his deck that said, used to

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take a village, now it takes
an ecosystem, right, So things are

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changing. Now you can just go
with get lab or get hub. But

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probably if you want to do continuous
deployment, you're going to want to partner

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with someone, So it might be
Armory. There are other firms. Circle

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cis one. I know of some
others out there as well, and of

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course they all specialize in a certain
way. But with that, let's bring

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in our friend Adam Frank from Armory. Adam, tell us a bit about

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yourself and while you're now focused quite
keenly on continuous deployment, Thanks for having

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me, Eric, Yeah, so
continuous deployment is really at the heart of

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you know, where I'm at and
where Armory is at as a company.

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And my story actually traces all the
way back to childhood. I won't tell

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you my whole childhood story, but
the summary of it is really my father

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was part of a development team,
and you know, they would get paid

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late at nights when things would go
wrong, and then of course it would

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take a long time to figure out
which change was it, how did that

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change get out there? And when
you start to look at automating that deployment

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process and making it safe, making
it reliable, you can really speed up

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your time to market, speed up
your innovation, make sure that you have

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the agility to make the moves that
you need to make, and just generally

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get more time and back in your
and stay asleep at night as supposed to

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change well, and you just get
a real big issue here, which is

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trouble shooting. That's one of the
biggest and hardest issues that developers space,

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I think is debugging code. One
of my best friends is a decades long

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developers like I don't want to debug
someone else's code, right because it's even

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harder because you don't even know what
they wrote or how they wrote it,

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or what they're trying to accomplish.
And typically developers don't like to spend time

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writing down what they've done, right. Documentation is not fun, which is

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why you're seeing a lot of that
automated now. And as I think about

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what's happening here, I think the
key is that we've figured out better and

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better ways to facilitate development process and
to point out errors. That's I mean

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even simple things, for example,
like an error on your screen when you

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haven't closed a hashtag for example,
or you haven't closed a bracket. I

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should say to say, hey,
you've got to close this bracket. That's

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very simple stuff. But we're now
into fairly complex stuff that could be automated

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and ascertained early that something is wrong. Correct. Yeah, absolutely, I

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mean that code that they are writing, those those errors that they need early

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on and upfront. Once that artifact
has been created from the CI process,

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that needs to then get deployed to
a number of environments. And we we've

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got customers and there's companies out there
that are deploying code around the globe,

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many different geographies, many different companies, countries, many different demographics. So

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there is a lot of process and
a lot of complexity that goes into even

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small companies sometimes, and that has
to be reliable, that has to be

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safe, that has to work.
Yeah, well, you know, you

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were kind of reminding me. For
folks who are not developers, it's kind

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of like having Grammarly or a spell
check in your word processor. It's going

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to throw up flags at you as
you're typing and saying, oh, this

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is a misspelled word or is it
a misspelled word? That's kind of what

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we're talking about, right, aid, Well, of course, get help

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now as copilot, which will suggest
things for you, just like Gmail.

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I noticed a couple of years ago
started on this track and this might have

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been a large language model underneath.
I'm not sure, but you would be

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typing an email in Gmail, like
in the actual web client, and it

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would complete sentences for you when you
could just choose to accept what it written,

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or you could type it out to
yourself. For me, I'm a

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typist, so it's just as easy
for me to type the letter as before.

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You know, just going to okay, accept this thing. But it's

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similar to that, right, It's
similar in that you've got some sort of

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guardrails as you're developing to help you
avoid making mistakes or solve problems before they

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become significant, right, Yeah,
Yeah, absolutely. I also like to

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make the generalization of the manufacturing manufacturing
industry and how they've automated so much of

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their process to remove some of that
human error and make sure that the quality

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is there, the quality checks are
there, so that something is consistently coming

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out at the end of that process
every single time. There's there's really no

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different here. There's a lot of
like I said, there's a lot of

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complexity. There's a lot of different
geographical compliance requirements and things like that.

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So it's really about orchestrating that artifact
that has been produced by CEI, tool

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that code and deploying it out across
these different environments. You've got to hook

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into things like security scanners, make
sure that code is secure signed, it

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is the code that you said is
going to be deployed. You've got to

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run integration tests on it, looking
at your observability data, making sure that

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the baseline of that customer experience is
still within the acceptable baseline. As you

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roll out to more and more traffic, more and more users within these different

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regions and around the different environments that
you are deploying to. At any point

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in time, if any of those
fail or if it comes out of bounds

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from that accepted user experience, you've
got to stop that deployment. You got

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to potentially roll back and then figure
out what happened, why it happened,

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and leveraging some of the error messaging
like you said before, Yeah, cluse

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those hints to make sure that you
can move forward quickly. Well, And

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so that's an excellent analogy, by
the way, I'm going to remember that

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when I'm using myself, because you're
right, manufacturing, you have all these

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processes that lead up to the production
line and all that stuff has to be

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correct, and then on the production
line it has to be correct as well.

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And you don't want to wait until
ten cars have come off the line

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to figure out that they don't have
stereos in them or that there's some part

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missing. Right, So you have
these checklist chip automated processes making sure that

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it's all going as it's supposed to
go. And you know, I'm thinking

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to myself, the development world has
become so complex. I mean, we've

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had integrated development environments for decades now. I'd mentioned to you before the show,

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the Eclipse Foundation that was one of
these IDEs, and you're seeing a

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real maturation in that space right now. And the idea is to facilitate productivity,

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so to identify issues, errors,
bugs before they get too far down

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the line, because once it's in
production now you've got a whole separate mess

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to solve. And so what you're
really trying to do is optimize the productivity

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of the developers and watch out for
any coding errors, any security flaws,

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things of this nature as much as
you can check as possible before you go

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over that wall into deployment. You
want to be able to handle all that

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stuff as dynamically and automated a process
as possible, right exactly. And then

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likewise with the deployment process, that
you want that as automated as possible as

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well. And that ten cars coming
off the line missing the steering wheel,

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we can relate that back to the
waterfall approach that you talked about earlier.

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Today's world, you want to tighten
that feedback loop. You want to start

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to get feedback from your users.
Are they using the feature, how are

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they using the feature, which way
should be lean more into should we be

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moved the button from the left to
the right as an example, it was

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something very simplistic. But you want
to continue to build builds things out as

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you go to make sure that you
are building the right things and you are

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staying competitive and really focusing on your
competitive advantage. Yeah, and you know

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you'd mentioned before the show site reliability
engineers. Right at the sr S,

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there are the folks that are sitting
out there trying to make sure that the

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site is working properly, that your
website is working properly, the functionality of

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the y all that kind of fun
stuff, and that's a huge part of

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the process. And you're really trying
to enable those people to be able to

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get two problems quickly or ideally identify
them before they go out the door.

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And you know, I know one
of the tactics used by large organizations is

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to deploy an in tiny little increment, so to deploy to one percent,

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for example, of your traffic on
a particular website and kind of see is

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that something you guys recommend as well, the sort of incremental deployment test one

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percent of the browsers and if there
are no problems up to ten percent of

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there's no problems over the fifty percent. Is that's something you guys do,

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Yeah, most definitely. We integrate
and we support a very variety of service

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meshes. When you are leveraging a
service mesh, you can control down to

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that one percent increment, like you
said, so starting out at that one

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percent, increasing it to five,
ten, twenty five, fifty, and

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as you increase, that's automated as
well. So it's looking at your observability

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data and the baseline of that data, keeping things within that baseline. If

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everything continues to be okay, it
continues to do log checks, there's no

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error messages, and it will automatically
increase that increment of that traffic. So

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your deployment is saling more and more
and more until it's eventually done and your

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fully in production and all your users
are leveraging it. And how do you

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deploy via service mesh? This is
one of these relatively new concepts. Of

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course, we're also up data mesh
on the other side and the data side,

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but a service mesh, what do
you need to deploy in order to

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do that? How does that actually
happen? So our solution integrates directly with

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that service mash. It integrates directly
with your cisoole and it integrates directly with

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your observability data. So what we
basically do is we have a connection with

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your Kubernet's cluster and we would then
take that artifact from the CEI solution and

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as your SLASH is integrated within of
your Kuberneties clusters for your traffic, we

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would have that communication to or from
every single Kuberneties cluster then all those Kuberneties

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clusters and start to leverage that service
mess as the traffic control. Really so

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we would start to increase it,
like I said, one five percent,

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ten percent, more and more and
more, either focal focal point on a

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single environment and then moving on next
environment, or doing multiple environments at once

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and then increasing to the next set
of environments. Yeah, that does is

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fascinating stuff. And you know,
Kubernetes really did revolutionize it did enterprise software,

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right. I mean, they've basically
won from what I can see.

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There are other ways that you can
do it. There's a joke about why

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Docker didn't win. I'd be curious
to hear your thoughts on this. One

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of the theories I heard as to
why Kuberneties, one which of course uses

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Docker. Someone told me is because
developers don't want to pay for anything,

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and doctor was trying to charge developers. What have you heard on that?

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I mean, there is there is
a number of conspiracy theories out there.

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I think, you know, coming
out of Google, there's certainly a number

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of surrounding Google being one of the
largest technology companies on the planet as well,

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and you know they're really wanting to
be in control of all of that

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orchestration in that But like I said, those are rumors and conspiracy theories,

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so one may never know. But
I think as a as an orchestration layer,

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it is. It is certainly taken
over the globe and really relolutionized the

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way that we we do build software
and manage software. Yeah. Well,

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and it also created the whole observability
eCos it seems to me, because even

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though it's extremely good at container orchestration, it raised all sorts of other issues

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around security in particular and governance.
And that's why you have all these observability

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vendors right to track to pull data
from these clusters and analyze it, companies

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like data Dog and New Relic,
and it's just a whole host of them

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out there, but to be able
to analyze what's happening and then give visualization

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and representation to the developer group of
what's actually going on and how they can

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fix things right. And then you
look for the pods that have installed,

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for example, and just kill those
pods and keep the ball rolling. Is

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that about right? Is that why
we're seeing so much in the observa ability

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spaces because Kuberneties is so complex and
spins out so much data, so much

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data, it's so complex. But
also we're in the world where we need

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to be proactive. Now, being
reactive is going to be a detriment to

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that competitive advantage. I mean,
it's so easy for you and I to

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be using one service one day and
have a bad experience with it and then

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download another app or use another service
that's providing us a better experience. It's

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just so easy for consumers and customers
to switch from one to the other these

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days that you have to maintain that
competitive edge. So observability really allows you

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to observe the internals of the systems, so you can understand what is happening

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to a lot deeper of a lot
deeper analysis and you can make decisions into

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that. Again, back to the
development process, even when you are developing

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that code, you can start to
understand what users are doing a lot more

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and being a lot more proactive about
it. Monitoring absolutely still exists. You

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still have to monitor your observability data. There's still a reactiveness that happens in

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software's. Things do break from time
to time. It's really about being proactive

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and understanding the experience that you are
building as you are building it, as

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opposed to supposed to be reactive.
I think the other thing that that you

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know, we can talk about with
toobreadies as well as tooberdies goes hand in

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hand with being declarative. You know, that kind of opened up a whole

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other world of developer simplicity as well
as opposed to being imperative and stating exactly

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how you want every little piece of
something to happen. Right, it's now

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brought into the declarative nature of things
that you can state the outcome and that

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engine, that declarative engine will start
to figure it out for you. Yeah,

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and that just blows my mind,
by the way. I'd like to

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get into that a little bit here, declarative versus imperative. Like it said,

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imperative is when you just write out
the code, specifically telling a system

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what to do. Declarative is when
you declare the end state that you want

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the system to find, and then
it just goes about coming up with ways

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to do things. So it's very
good at making sure things get done.

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I have to believe this is a
bit of a performance tax, and they're

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somewhere. I mean, if you
really understand what you're trying to do imperative

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it could still make sense. But
in these highly complex environments, I guess

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that's why imperative is so valuable,
because it can sort through all the different

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possibilities of getting something done and then
of course test to see how fast it's

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happening and thus make optimized decisions.
Is that right? Yeah. Imperative is

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incredibly flexible. I mean, it
gives you a lot of control because you

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can, like I said, control
every single stage within that pipeline, explaining

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exactly how you want things to happen. But the ease of use is certainly

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with the declarative nature of this,
Especially with the declarative orchestration model, you

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cannot, like you said, state
the outcome that you would like in this

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example, the deployment strategy. You
want to use a canary strategy that's going

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to take your observability data. You
want it deployed out to these five environments

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in a way it goes. It
starts to figure out the best path for

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that deployment, starts to increase that
traffic to all of that stuff automatically.

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You've just stated the outcome as opposed
to stating how you want everything done.

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A colleague of actually has a fantastic
talk that relates imperative declarative to a grill

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chee sandwich. An imperative being explaining
exactly how you want this girl cheese sandwich,

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made, the ingredients, the process, everything, as opposed to declarative

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just stating I'd like a grill cheese
sandwich please, and you sandwich. Yeah,

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that really is amazing stuff. I'd
like to get deeper into that exactly

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how it works, but the point
is it works right. It reminds me

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of one of the funniest quotes I
ever heard was my buddy Nikolai Menshikov,

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a Russian who told me many years
ago, back in two thousand. In

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fact, he says to me,
the funny thing about the web is that

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no one knows how it works.
All we know is it works, and

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so you just kind of go from
there, like testing things just in terms

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of how everything connects and the flow
occurs. It's like, whoa, it

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gets the job done somehow, some
way, but folks don't touch that.

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00:17:56.519 --> 00:18:02.000
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back to Inside Analysis. Here's your
host, Eric Tavanaugh. Alright, folks,

299
00:22:12.799 --> 00:22:18.839
back here on Inside Analysis, talking
all about continuous deployment in the software

300
00:22:18.920 --> 00:22:22.240
world. We were just talking a
moment ago with our friend here, Adam

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00:22:22.400 --> 00:22:26.079
Frank, from a company called Armory, and they specialize in continuous deployment.

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00:22:26.559 --> 00:22:32.440
And this is a hot topic these
days because you have to be constantly watching

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00:22:32.480 --> 00:22:36.079
what's happening on your sites in your
business. You have to be able to

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00:22:36.160 --> 00:22:38.640
understand what's going on and then be
able to take action. And I think

305
00:22:38.680 --> 00:22:44.519
the demand for agility is what has
really fed a lot of this innovation.

306
00:22:44.519 --> 00:22:48.440
What do you think Adam. Yeah, absolutely, and people need to focus

307
00:22:48.480 --> 00:22:52.319
on those competitive advantages that we were
talking about and really focus a lot of

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00:22:52.359 --> 00:22:57.039
their business and their resources on their
competitive advantage and staying asile, making sure

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00:22:57.039 --> 00:23:00.960
that you can close that feedback loop
on your stand, your market, your

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00:23:02.039 --> 00:23:07.920
users, even where you're going next, is just vital to success and staying

311
00:23:07.920 --> 00:23:11.119
in live and thriving in this world. Though. Yeah. Well, and

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00:23:11.200 --> 00:23:15.000
you know, when you talk about
container orchestration, which of course is what

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00:23:15.079 --> 00:23:18.920
Kubernetes does, I think one good
analogy would be that in the old days,

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00:23:19.359 --> 00:23:23.119
your website could crash and it just
goes down. But if you have

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00:23:23.240 --> 00:23:27.400
one of these new environments where it's
underpinned by kuberneties instead of a traditional model,

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00:23:27.799 --> 00:23:30.359
you may get bits and pieces that
aren't working, and like maybe the

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00:23:30.359 --> 00:23:34.039
search won't work, or maybe a
certain button won't load, but overall the

318
00:23:34.079 --> 00:23:37.599
site keeps going. And I think
that's one of the benefits, right,

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00:23:37.720 --> 00:23:40.440
is that even if something does break, it's going to be one piece instead

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00:23:40.480 --> 00:23:42.680
of the whole thing crashing and burning. Is that right? Yeah? Absolutely,

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00:23:42.759 --> 00:23:47.960
microservice versus monolith, Yeah, precisely. And I'm actually I don't know

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00:23:47.960 --> 00:23:49.839
about you, but I've certainly run
into that experience where I've been on a

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website, I've been using a service
and everything's fine until you know your start

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00:23:55.079 --> 00:24:02.079
rage clicking that checkout buttons and one
of the pieces and working and rage clicking

325
00:24:02.119 --> 00:24:04.799
that's funny, that's a good band. Rage clicking, I go see them,

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00:24:07.440 --> 00:24:11.240
but yeah, so and then you
know, in terms of managing the

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whole process over time, I'm guessing
that armory and I'm sure some of these

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00:24:15.279 --> 00:24:21.400
other technologies are capturing a lot of
information about your environment right and what works

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and troubleshooting and when you find something
that goes wrong being able to identify that.

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00:24:26.400 --> 00:24:30.759
I mean, I've seen in some
of the newer plays around service management,

331
00:24:30.839 --> 00:24:33.680
basically, like the stuff that Service
Now does and these guys is you

332
00:24:33.960 --> 00:24:40.160
will have a bit of a playbook
generated for the SRIS that says, Okay,

333
00:24:40.160 --> 00:24:42.839
if this happens, check this,
check this, check that based upon

334
00:24:42.960 --> 00:24:45.880
things that have happened in the recent
past, or over the past year,

335
00:24:45.960 --> 00:24:49.640
or whatever the case may be.
Because you never know, like there's always

336
00:24:49.839 --> 00:24:53.279
some question as to what exactly is
going on. But if the sres have

337
00:24:53.319 --> 00:24:59.799
a playbook to look to that gets
fed by these algorithms, and human beings

338
00:24:59.799 --> 00:25:03.119
and acting with it. That's your
sort of go to strategy. Is that

339
00:25:03.240 --> 00:25:07.880
about right? I think a lot
of people are certainly leveraging strategy like that.

340
00:25:07.920 --> 00:25:11.599
As a continuous deployment company, we're
very keen on looking at that and

341
00:25:11.680 --> 00:25:15.559
integrating with some of that data that
does say something is going wrong here,

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00:25:15.640 --> 00:25:19.000
something is not right, whether it
be checking logs, whether it be looking

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00:25:19.039 --> 00:25:23.359
at metric data and baselining that,
we certainly want to take a look at

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that data during the deployment. As
traffic is increasing, as we're deploying from

345
00:25:27.920 --> 00:25:33.640
one environment to the next environment and
increasing global span of that deployment, we

346
00:25:33.759 --> 00:25:38.839
absolutely want to take a look at
that data and have it automatically analyzed using

347
00:25:38.880 --> 00:25:45.480
statistical analysis and other methods to make
sure that everything is going to be safe

348
00:25:45.480 --> 00:25:52.279
and sound with that deployment. Yeah. Now, in terms of market factors

349
00:25:52.359 --> 00:25:56.480
like the cloud, cloud computing fundamentally
changing everything that things are changing all the

350
00:25:56.519 --> 00:26:02.400
time. Microsoft just announced Microsoft Fabric, which sounds pretty interesting. AWS is

351
00:26:02.440 --> 00:26:07.599
always changing things, always coming up
with new services, etc. And there's

352
00:26:07.599 --> 00:26:11.720
this whole move to the cloud from
on prem. Do you deploy primarily on

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prem or can you actually be used
in an environment to facilitate a move to

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the cloud where and when that's appropriate. We deployed on prem we deployed the

355
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clouds. It's really about having a
mantra that we believe in and meeting our

356
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customers where they are. And I
think a lot of people these days are

357
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really going with a hybrid strategy that
they've got a number of things that are

358
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on prem while they've got a number
of things that are in the cloud as

359
00:26:36.720 --> 00:26:40.519
well. Certainly, some companies that
have been around much much longer, they

360
00:26:40.599 --> 00:26:45.160
have to evaluate the return on investment
for taking something that's on prem and you

361
00:26:45.200 --> 00:26:48.759
know, we are architecting it for
the cloud, or even perhaps we are

362
00:26:48.880 --> 00:26:52.720
taking it to be cloud native.
You know, one of our customers,

363
00:26:52.839 --> 00:26:56.400
you know, it's a forty year
old plus customer and you know, with

364
00:26:56.559 --> 00:27:00.119
their core piece of software. We
sat on with them, we talked about,

365
00:27:00.160 --> 00:27:03.200
you, what's the likelihood that you
would ever do something like this and

366
00:27:03.480 --> 00:27:07.160
move to the cloud and cloud data, and it's like, well, we

367
00:27:07.240 --> 00:27:11.960
whipped the return of investagers wouldn't be
there. Where's millions of lines of code

368
00:27:11.960 --> 00:27:15.359
here and it's a forty year old
plus company. By the time we did

369
00:27:15.359 --> 00:27:18.119
this maybe forty years in the future
from now, and we'd be talking about

370
00:27:18.119 --> 00:27:25.519
the exact same thing all over again. That's pretty funny. Yeah, I

371
00:27:25.559 --> 00:27:29.480
mean, you know, and that's
the sort of monolithic approach. You know,

372
00:27:29.519 --> 00:27:33.039
when I look at the broader market, you look at an SAP for

373
00:27:33.079 --> 00:27:37.640
example, which is a monolithic approach. Now, I've been reading that they're

374
00:27:37.640 --> 00:27:41.200
getting a bit more friendly with Kubernetes
deployments. They're looking at ways to break

375
00:27:41.240 --> 00:27:45.480
all that stuff down. But that's
a very difficult thing to break down,

376
00:27:45.599 --> 00:27:48.000
right. The larger it gets,
the harder it is to kind of sort

377
00:27:48.000 --> 00:27:52.960
of deconstruct and then reconstruct. So
to me, I think cloud is going

378
00:27:53.000 --> 00:27:57.559
to be largely net new use cases
or new ways of doing old things.

379
00:27:59.079 --> 00:28:00.920
But you know, in terms of
taking on prem code and trying to port

380
00:28:00.920 --> 00:28:04.599
it into the cloud, that's a
very tricky situation. It's really not typically

381
00:28:04.640 --> 00:28:08.559
a good idea, right, Yeah, and again it has to be evaluated,

382
00:28:08.599 --> 00:28:11.319
and you know, sometimes that evaluation
may turn out that, yeah,

383
00:28:11.319 --> 00:28:14.759
it's a good idea, I think, you know a lot of the times

384
00:28:14.799 --> 00:28:18.279
that we've seen you know, it
may it may not be. So you

385
00:28:18.319 --> 00:28:22.559
know, we really want to be
meeting our customers where they are and deploying

386
00:28:22.759 --> 00:28:26.119
both on prems, deploying to the
cloud with kubernet is really being at the

387
00:28:26.160 --> 00:28:30.519
forefront right now. But even everybody
that's leveraging Kubernetes that we talked to as

388
00:28:30.519 --> 00:28:37.400
well, still has some bit of
servilists or other components within their stack that

389
00:28:37.599 --> 00:28:41.480
isn't Kuberneties. Some of them might
be migrating it to Kubernetti, some of

390
00:28:41.519 --> 00:28:45.920
them might have a servialist component for
very very valid reasons that they want to

391
00:28:45.240 --> 00:28:48.240
grow as well. So being able
to deploy the servilest, being able to

392
00:28:48.279 --> 00:28:52.079
deploy the Kuberneties, you know,
these are all very very important things for

393
00:28:52.279 --> 00:28:57.680
their developers and for our development community
to maintain their agility and increase their innovation

394
00:28:57.720 --> 00:29:02.440
and time to market. So right, and then AI ops is something else

395
00:29:02.480 --> 00:29:06.400
you folks are talking about. And
now thanks to these large language models like

396
00:29:06.480 --> 00:29:08.680
chat, GPT, all of a
sudden, the entire world has figured out

397
00:29:08.720 --> 00:29:12.240
that AI is real. Now,
it wasn't the first AI solution, but

398
00:29:12.559 --> 00:29:18.680
it's certainly the most broadly appreciated in
terms of its impact and its value.

399
00:29:18.079 --> 00:29:23.480
Now there are at some gotchas in
there. These large language models will hallucinate

400
00:29:23.519 --> 00:29:26.759
as the term it gets used.
I think that's pretty funny, and others

401
00:29:26.799 --> 00:29:29.920
they just make stuff up right there, grabic it's a basic it's a predictive

402
00:29:29.960 --> 00:29:33.079
engine for language. So you prompt
it and then it will give you an

403
00:29:33.119 --> 00:29:38.000
answer based upon what it thinks.
You want to see that from its huge

404
00:29:38.599 --> 00:29:44.160
troves of information. But there's a
bit of a black box issue going on,

405
00:29:44.240 --> 00:29:47.680
and with artificial intelligence, we want
to know, you want explainability.

406
00:29:48.279 --> 00:29:52.440
Where do you fit into the AI
ops equation? Is it that you're bringing

407
00:29:52.519 --> 00:29:59.160
that regimented approach of deployment to the
rather unwieldy world of AI and that's how

408
00:29:59.200 --> 00:30:02.039
you can kind of an gate some
of these problems or what's your story there?

409
00:30:02.720 --> 00:30:07.519
I think deployment has really evolved from
the imperative nature is kind of where

410
00:30:07.519 --> 00:30:10.920
it started building out pipelines, building
out every single stage of that pipeline,

411
00:30:11.279 --> 00:30:15.240
to now the declarative nature that Kupernetes
has brought into the world. We are

412
00:30:15.480 --> 00:30:22.480
very much focused on delivering a pop
shelf experience that is declarative, kind of

413
00:30:22.480 --> 00:30:26.720
that get ups term that it's been
thrown around and coined by another company,

414
00:30:26.480 --> 00:30:32.440
making sure that our users can declare
their outcome that they want and that's what

415
00:30:32.519 --> 00:30:34.720
they will get with that, so
they can really focus on writing code as

416
00:30:34.759 --> 00:30:37.799
a point of figuring figuring out how
to deploy that code. But I think

417
00:30:38.200 --> 00:30:45.039
generative AI certainly has the potential to
be that next evolution from declarative. Like

418
00:30:45.200 --> 00:30:48.640
you said, there's a lot of
explainability and things like that that people do

419
00:30:48.799 --> 00:30:53.480
want. But we've certainly been playing
around with generative AI in the deployment space

420
00:30:55.119 --> 00:31:00.000
and we're able to produce some pretty
cool configuration and it's pretty cool artifacts well.

421
00:31:00.279 --> 00:31:07.200
So this is getting really interesting from
my perspective because when you start thinking

422
00:31:07.240 --> 00:31:12.599
about the different ways that you can
deploy software. You've mentioned somebody's paradigms already,

423
00:31:12.680 --> 00:31:18.200
server lists versus Kubanetti's traditional monolithic architectures. There are lots of ways you

424
00:31:18.200 --> 00:31:21.519
can get things done, and there
are also lots of ways you can optimize

425
00:31:21.519 --> 00:31:25.799
things. So if you look at
what VMware did to traditional computing, while

426
00:31:25.839 --> 00:31:33.000
they really took this amazing new novel
approach to optimize the use of hardware,

427
00:31:33.640 --> 00:31:36.880
right, so what they were doing
communities is not the same, but it's

428
00:31:36.920 --> 00:31:40.240
sort of the same objective that you're
trying to accomplish. But when I think

429
00:31:40.279 --> 00:31:45.200
about the different deployment options that you
have and where functionality actually gets done,

430
00:31:45.279 --> 00:31:49.839
where functionality is done versus where data
is delivered, for example, it's a

431
00:31:49.920 --> 00:31:55.720
tremendously wide tapestry of how you can
accomplish some stuff. What are your thoughts

432
00:31:55.759 --> 00:32:00.119
about that, and what are your
thoughts about trying to explain to people why

433
00:32:00.200 --> 00:32:04.680
it matters and what benefit you can
get by using these technologies in terms of

434
00:32:04.920 --> 00:32:08.839
optimizing productivity, reducing risk, all
these kind of things. How do you

435
00:32:08.880 --> 00:32:13.440
explain that to people? Because it
is very complex, But it's just it's

436
00:32:13.480 --> 00:32:16.680
a question of different layers in the
architecture and what gets done at this layer.

437
00:32:16.680 --> 00:32:20.079
Because it used to be just the
OS, right you had your operating

438
00:32:20.079 --> 00:32:22.160
system on your computer and you had
to update your operating system and you had

439
00:32:22.160 --> 00:32:24.960
apps that it had be written for
that OS, etc. Well, now

440
00:32:24.960 --> 00:32:29.839
we have kubinelities, we have serverists, we have other ways of doing things.

441
00:32:30.480 --> 00:32:32.160
Still stuff is getting done, code
is getting processed, etc. But

442
00:32:32.200 --> 00:32:37.319
a question becomes where in the sort
of workflow do you execute this functionality versus

443
00:32:37.319 --> 00:32:40.240
that functionality? Where do you pull
in data solve it that way? I

444
00:32:40.279 --> 00:32:45.720
mean, there's an infinite number of
possibilities, but how can you make sense

445
00:32:45.759 --> 00:32:50.559
of all that for an enterprise deployment
for say like an insurance company for example.

446
00:32:50.559 --> 00:32:53.319
Can you give us to some color
or context about how you would explain

447
00:32:53.359 --> 00:32:58.079
to a client the different ways you
can do things and how you can optimize

448
00:32:58.119 --> 00:33:01.880
what they're doing. Yeah, I
think we would really start by talking about

449
00:33:01.920 --> 00:33:06.079
their developers and their developer experience that
they have today. How much do the

450
00:33:06.119 --> 00:33:08.680
developers have to know, how much
the developers have to get their hands in

451
00:33:08.720 --> 00:33:14.880
there, how much time are the
developers taken away from writing the code that

452
00:33:14.920 --> 00:33:19.599
they love writing and innovating as opposed
to figuring out all of those different layers.

453
00:33:19.599 --> 00:33:22.839
We just talked about the infrastructure and
like how to actually deploy their software,

454
00:33:23.240 --> 00:33:27.680
So we talked about it in terms
of abstraction. So when we look

455
00:33:27.680 --> 00:33:30.400
at different layers, we want to
talk about the abstraction that's put on top

456
00:33:30.440 --> 00:33:36.440
of that layer to really make the
developers lie easier and have a fantastic developer

457
00:33:36.480 --> 00:33:40.799
experience. And that's really where the
declared of orchestration all that we delivered has

458
00:33:40.880 --> 00:33:50.440
come in, so being able to
take the outcome that they want really provides

459
00:33:50.480 --> 00:33:53.960
that layer of abstraction on top of
their Kuberneties, on top of their servi

460
00:33:54.039 --> 00:33:58.680
lists, so they can write their
code. They can have the CI system

461
00:33:58.759 --> 00:34:04.079
produce the artifact, and then that
artifact gets deployed out to the different infrastructure

462
00:34:04.119 --> 00:34:07.159
that's around there. Now, typically
they would have a platform team. That

463
00:34:07.239 --> 00:34:12.559
platform team might be involved with all
of the infrastructure or certain elements of the

464
00:34:12.599 --> 00:34:16.400
infrastructure. They might have different infrastructure
teams. That's kind of specialize in that.

465
00:34:17.039 --> 00:34:22.440
But we really want them to focus
on writing that code and having that

466
00:34:22.519 --> 00:34:27.360
layer of abstraction just to make that
nice and easy. Yeah, that's interesting.

467
00:34:27.480 --> 00:34:31.760
This is just fascinating stuff. So
to your point, there are tons

468
00:34:31.800 --> 00:34:36.320
of different ways that you can do
development. There are tons of different ways

469
00:34:36.400 --> 00:34:40.400
to build your teams to orchestrate things, which tools and technologies you use,

470
00:34:40.880 --> 00:34:45.719
which languages you use for crying out
loud. And now at these large language

471
00:34:45.719 --> 00:34:49.280
models, well guess what they can
write code too? Right? That was

472
00:34:49.360 --> 00:34:51.519
my first thought as like, wait
a minute, if this can write in

473
00:34:51.559 --> 00:34:54.480
English, I'll bet it can write
in code. Yes it can. Oh

474
00:34:54.599 --> 00:35:00.159
that's an interesting factor too, right, because now business people could say,

475
00:35:00.639 --> 00:35:05.679
give me some coball code that would
process transactions and send a report to this

476
00:35:05.800 --> 00:35:09.079
thing over here, and they'll go
and give you a basic set of code.

477
00:35:09.480 --> 00:35:12.719
Now that does need to be vetted. I've heard from a lot of

478
00:35:12.760 --> 00:35:16.400
people that it'll get things close but
not exactly right, and so you do

479
00:35:16.519 --> 00:35:20.920
have to do some finishing touches.
But nonetheless, that's a big change in

480
00:35:20.960 --> 00:35:22.960
the marketplace, right to have any
number of people able to just kind of

481
00:35:23.000 --> 00:35:27.400
spin up big bits of code.
Are you seeing that yet in the marketplace

482
00:35:27.480 --> 00:35:30.440
or is that still a bit far
off? I think it's still a bit

483
00:35:30.519 --> 00:35:32.280
far off. There's certainly a number
of people that are playing with it and

484
00:35:32.360 --> 00:35:37.320
producing it. Like one of the
areas that we as a company have found

485
00:35:37.880 --> 00:35:42.000
a good use of it. We
are also a global company. English is

486
00:35:42.000 --> 00:35:46.440
not the first language for a number
of our employees, so the ability to

487
00:35:47.239 --> 00:35:52.880
produce comments around some of that code
has been fantastic. You know. It's

488
00:35:52.920 --> 00:35:57.079
really produced with interesting, great English
around that code and made it really easy

489
00:35:57.599 --> 00:36:00.880
for some of our developers where English
is not there language. So that's that's

490
00:36:00.920 --> 00:36:06.039
kind of where we found a fantastic
you so far, but certainly playing with

491
00:36:06.199 --> 00:36:08.360
producing bits of code, Like you
said, it's it's absolutely going to be

492
00:36:08.440 --> 00:36:15.599
vetted. And how that code all
comes together to produce a feature, to

493
00:36:15.639 --> 00:36:20.079
produce a product. I mean,
that's a much much larger topic, and

494
00:36:20.519 --> 00:36:23.519
yeah, certainly get into it.
But well, I was just reading Aligadzi

495
00:36:23.599 --> 00:36:29.599
from Databox was writing the day use
cases for LMS for large language models,

496
00:36:29.599 --> 00:36:34.960
and he said one of them is
just a Basically I helped ask right to

497
00:36:35.000 --> 00:36:37.280
just explain to you what's going on
now. I think we are going to

498
00:36:37.400 --> 00:36:42.280
see these single tenant versions come out
where you'll be able to have your own

499
00:36:42.400 --> 00:36:45.119
LM for your enterprise and plug it
into your system. So it's not just

500
00:36:45.119 --> 00:36:49.519
connecting to the world because there are
some security issues and privacy issues, etc.

501
00:36:50.679 --> 00:36:52.880
But nonetheless, what kind of gets
me. Maybe it's my last question

502
00:36:52.920 --> 00:36:57.760
for you is how good do you
think these large language models will be at

503
00:36:57.920 --> 00:37:05.400
understanding steady and configurations and thus give
feedback to developers about how to configure these

504
00:37:05.440 --> 00:37:08.159
new environments. What do you think
about that. I think they will be

505
00:37:08.679 --> 00:37:13.880
I think I think people will put
the time and effort into training them.

506
00:37:13.920 --> 00:37:16.440
I think people will also put the
time and effort into understanding how to write

507
00:37:16.519 --> 00:37:22.800
proper prompts and think that that will
actually that will produce pretty good quote,

508
00:37:22.840 --> 00:37:27.199
pretty good, pretty good configuration and
things like that. So I think they

509
00:37:27.239 --> 00:37:31.440
will, especially within smaller environments where
like you said, they're isolated and you

510
00:37:31.480 --> 00:37:36.599
know, has to do with that
particular business, there's less, less training,

511
00:37:36.719 --> 00:37:39.719
less kind of pathways that can deviate
there. So I think they will

512
00:37:39.880 --> 00:37:44.239
certainly to do well. This is
super cool stuff. Will folks who've been

513
00:37:44.239 --> 00:37:49.280
talking to Adam Frank in a very
candid conversation about the modern world of continuous

514
00:37:49.320 --> 00:37:52.000
deployment, Look these folks up online. Armory is the company and he's on

515
00:37:52.119 --> 00:37:57.440
LinkedIn. Adam Frank will be right
back. You're listening to Inside it Out.

516
00:38:00.760 --> 00:38:05.360
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Welcome back to Inside Analysis. Here's
your host, Eric Kavanaugh. All right,

567
00:42:43.599 --> 00:42:47.280
pok back upon himsialysis. Who's Aaron
Kavanaugh Here. We'll be talking all

568
00:42:47.280 --> 00:42:54.960
about software deployment, enterprise software design
and deployment, continuous integration, continuous delivery

569
00:42:55.440 --> 00:42:59.519
with our friends from Armory. And
for the next couple of segments, I

570
00:42:59.519 --> 00:43:04.880
wanted to die into the whole topic
of Kubernetes and modern software development. What

571
00:43:04.960 --> 00:43:08.199
has changed, why it's changing,
how that affects you as an end user,

572
00:43:08.199 --> 00:43:10.840
how it affects you as a business
person, how it affects you as

573
00:43:10.840 --> 00:43:16.280
a potential coder out there in the
marketplace. Here in Pennsylvania, there was

574
00:43:16.920 --> 00:43:22.119
the Xcel pipeline was killed a while
ago, and some people were, i

575
00:43:22.159 --> 00:43:25.480
think ironically joking, oh, learn
to code, suggesting that the folks who

576
00:43:25.480 --> 00:43:29.679
were going to work on that project
could just go learn to code. Well,

577
00:43:29.679 --> 00:43:31.960
the truth is these days they probably
could. Right. First of all,

578
00:43:32.000 --> 00:43:36.360
anyone can code. It's it's like
writing. Most people know how to

579
00:43:36.400 --> 00:43:38.519
write in a language, so you
can learn how to write encode. It

580
00:43:38.599 --> 00:43:43.760
seems very daunting at first, perhaps, and certainly some of the more advanced

581
00:43:43.760 --> 00:43:47.639
work that gets done these days in
artificial intelligence and machine learning, even just

582
00:43:47.679 --> 00:43:52.800
setting up infrastructure as code as they
call it for the business applications that are

583
00:43:52.840 --> 00:43:57.400
running today. All that stuff can
get very complex, but that doesn't mean

584
00:43:57.440 --> 00:44:00.559
you can't learn to code. Any
One can, frankly, if they have

585
00:44:00.719 --> 00:44:05.679
time and effort in the interest level. It takes commitment, It does take

586
00:44:05.719 --> 00:44:08.800
some dedication, It takes patience,
I think more than anything, it takes

587
00:44:09.360 --> 00:44:15.679
an engineering mindset to tackle some of
these challenges to figure out how you're going

588
00:44:15.719 --> 00:44:19.960
to be able to struggle through it, so to speak. But we're going

589
00:44:20.000 --> 00:44:23.039
to talk a little bit about large
language models. So of course chat GBT

590
00:44:23.280 --> 00:44:29.280
is taking the industry by storm.
Dolly two point zero came out from Data

591
00:44:29.280 --> 00:44:32.639
Bricks. There are several other of
these large language models out there, and

592
00:44:32.679 --> 00:44:38.760
they're very interesting things. So they
are text engines, text generative engines,

593
00:44:38.800 --> 00:44:43.800
if you will. There's a term
generative AI that gets thrown around a bit

594
00:44:44.320 --> 00:44:47.760
on fact yours truly and doctor Robin
Blore. We're working with a company called

595
00:44:47.800 --> 00:44:52.559
Praxi Data right now, very interesting
company. P r a Xi Data will

596
00:44:52.559 --> 00:44:58.199
have a paper coming out on this
very topic on the challenges of generative AI.

597
00:44:58.840 --> 00:45:01.079
I'm joking. The titles ship be
generative ais that a promise or a

598
00:45:01.199 --> 00:45:06.559
threat, going back to our childhood
days when we would say that as a

599
00:45:06.639 --> 00:45:12.280
taunt to someone where someone was let's
say you either promising something sketchy or threatening

600
00:45:12.360 --> 00:45:15.119
something silly, and we joke.
Is that a promise or a threat as

601
00:45:15.159 --> 00:45:20.280
a way of sort of diffusing the
situation, I guess, or dealing with

602
00:45:20.320 --> 00:45:25.119
it. But there are very many
promising use cases around generative AI. And

603
00:45:25.239 --> 00:45:30.719
it can write code. So here's
the thing. It doesn't just write poems

604
00:45:30.760 --> 00:45:36.360
and sonnets and research papers and school
papers and so forth. It writes actual

605
00:45:36.440 --> 00:45:38.880
code and lots of different languages.
It can write, as I understand it,

606
00:45:38.920 --> 00:45:43.719
in Cobal. It can write in
Basic, and write in Pythonic and

607
00:45:43.800 --> 00:45:47.719
write in Java. Well what does
that mean for the software development industry?

608
00:45:47.719 --> 00:45:51.320
And I also wanted to talk about
shift left versus shift right, just so

609
00:45:51.400 --> 00:45:54.519
you have some of these terms in
your mind. But let's first talk about

610
00:45:54.719 --> 00:46:00.280
actual code generation. Well, this
is some very interesting stuff. So there

611
00:46:00.280 --> 00:46:05.480
are also two main kinds of programming. You heard as talking in the last

612
00:46:05.480 --> 00:46:12.880
couple of segments with Armory about imperative
versus declarative. Imperative programming is where you

613
00:46:12.880 --> 00:46:16.280
write out, step by step the
specific instructions that you want the application to

614
00:46:16.320 --> 00:46:22.159
do. Declarative is much different than
that. Declarative You say what you want

615
00:46:22.159 --> 00:46:25.079
the end result to be and let
the engine write the code on the fly

616
00:46:25.719 --> 00:46:30.079
to do the job for you.
While these new engines, these new or

617
00:46:30.159 --> 00:46:34.400
large language models are declarative in nature, at least I'm pretty sure they are,

618
00:46:34.920 --> 00:46:38.199
and so they can come up with
different answers. You can actually do

619
00:46:38.239 --> 00:46:42.400
it twice in a row and you'll
get a different set of code solving something

620
00:46:42.440 --> 00:46:46.000
in a different sort of way.
Well, this actually speaks to the I

621
00:46:46.039 --> 00:46:51.679
guess, promise and threat that the
blessing and the curse of writing enterprise code

622
00:46:51.719 --> 00:46:55.719
these days are writing code for applications
and for systems is that there are so

623
00:46:55.760 --> 00:47:00.639
many ways to get things done.
So typically what you have to do us

624
00:47:00.719 --> 00:47:04.280
understand, all right, what is
the code based that we're working on at

625
00:47:04.280 --> 00:47:07.559
this company, What are our developers
like, what tools do they like to

626
00:47:07.679 --> 00:47:10.800
use, what licenses do we have? There are lots of different ways you

627
00:47:10.800 --> 00:47:15.320
can do things. We were talking
about integrated development environments IDEs. The Eclipse

628
00:47:15.360 --> 00:47:21.000
Foundation, for example, has been
around for a long time and their vision

629
00:47:21.360 --> 00:47:24.599
was to create a resource for developers
to help developers. Now, of course

630
00:47:24.639 --> 00:47:29.159
we have all these engines like GitHub
and get lab and others, and with

631
00:47:29.239 --> 00:47:32.639
GitHub you've got this thing called Copilot, and Copilot will just go along with

632
00:47:32.679 --> 00:47:37.920
you and help you write code.
So this generative AI thing has really gone

633
00:47:37.920 --> 00:47:40.920
from zero to a thousand miles an
hour and almost no time. And you

634
00:47:40.920 --> 00:47:45.760
can tell it's a big deal because
all of the software vendors are talking about

635
00:47:45.760 --> 00:47:51.239
it. I mean software AG back
in February had just announced the connector to

636
00:47:51.719 --> 00:47:55.480
chat gpt or to the open AI
platform, and that was pretty early in

637
00:47:55.519 --> 00:47:58.719
the game. They were one of
the first. Now everyone is i mean

638
00:47:58.760 --> 00:48:02.039
not everyone, but Darnira, everyone
is talking about ways to connect generative AI

639
00:48:02.440 --> 00:48:07.639
engines to their software applications, to
their technologies. Well why is that.

640
00:48:07.679 --> 00:48:12.559
It's because it's a really big deal
and it can do lots of interesting things.

641
00:48:12.559 --> 00:48:17.320
It can do a contextual analysis,
it can refine the creative content that

642
00:48:17.400 --> 00:48:22.079
it generates, it can do a
lot of different things. And there are

643
00:48:22.199 --> 00:48:28.079
open APIs coming up on a regular
basis to this system for ways to leverage

644
00:48:28.079 --> 00:48:30.960
its power. Even more so,
we're really kind of just at the beginning

645
00:48:30.519 --> 00:48:36.920
of these large language models, of
these foundational models, if you will,

646
00:48:37.440 --> 00:48:40.079
And what that means from a coding
perspective for you as a possible coder,

647
00:48:40.320 --> 00:48:45.519
is that you can get going right
away. You can have chat GPT create

648
00:48:45.599 --> 00:48:49.679
code for you and then have it
explained to you what the code is doing.

649
00:48:50.199 --> 00:48:54.960
You can have a try around different
tests and different mechanisms of doing something

650
00:48:55.360 --> 00:49:00.000
like a data quality filter, for
example, for your database. You can

651
00:49:00.039 --> 00:49:04.679
get really specific with these technologies and
it'll hack out code. Now, what

652
00:49:04.719 --> 00:49:07.079
I've been told by quite a few
people now who are doing this is that

653
00:49:07.119 --> 00:49:10.199
you do have to do some fine
tuning. So right out of the gate

654
00:49:10.360 --> 00:49:15.719
it's not necessarily going to be perfect, but at least it will be close.

655
00:49:16.400 --> 00:49:22.079
And so by giving you something to
work with, it's really expediting the

656
00:49:22.159 --> 00:49:24.719
process. Well guess what, that's
the same thing it does in the world

657
00:49:24.840 --> 00:49:30.719
of content creation for marketers and for
content people. Chat GPT can be very

658
00:49:30.800 --> 00:49:37.719
useful for generating ideas for tweets,
for example, because you can give the

659
00:49:37.760 --> 00:49:39.960
engine and you use a prompt is
how they describe it. It's kind of

660
00:49:39.960 --> 00:49:44.639
like Google. You click into the
Google prompt and you are type in your

661
00:49:44.639 --> 00:49:46.719
words and you get a search query. Well, with chat GPT, you

662
00:49:46.800 --> 00:49:52.880
put in your instructions because these are
instruction following models. Basically you put in

663
00:49:52.920 --> 00:49:57.199
your instructions and then it comes back
to you with an answer if you will

664
00:49:57.280 --> 00:50:02.480
to your request. So you can
write ten tweets about the power of Kubernetes

665
00:50:02.840 --> 00:50:07.599
to help business people. And I
actually just did this before doing this show,

666
00:50:07.679 --> 00:50:09.960
just to see how well it does. And it does very very well.

667
00:50:10.480 --> 00:50:15.239
So I prompted at saying kubernet is
whereas one of the top ten most

668
00:50:15.239 --> 00:50:21.400
important benefits of kubernets for developers and
for businesses, and within a couple of

669
00:50:21.440 --> 00:50:25.840
moments it banged out this top ten
list for developers. Scalability. Kubernatis endables

670
00:50:25.920 --> 00:50:31.440
developers to easily scale their applications horizontally
by adding or removing containers, ensuring optimal

671
00:50:31.480 --> 00:50:37.400
performance and resource utilization. Two automated
deployments, three, service discovery and load

672
00:50:37.440 --> 00:50:42.960
balancing. I mean load balancing in
and of itself has been an area of

673
00:50:43.559 --> 00:50:50.000
expertise and of study for since the
computer came out. Load balancing is all

674
00:50:50.039 --> 00:50:52.599
about, literally what it sounds like, balancing the load, the workload that's

675
00:50:52.599 --> 00:50:55.719
coming in that we have to handle. How can we balance it? Well,

676
00:50:57.280 --> 00:50:59.719
you know, you go back for
some ancient history here to like the

677
00:50:59.800 --> 00:51:04.800
year nineteen ninety eight, nineteen ninety
nine into two thousand, there were groups

678
00:51:04.840 --> 00:51:07.880
of folks at companies like Yahoo for
example, who were working intently and in

679
00:51:07.880 --> 00:51:13.760
fact, a lot of the open
source movements spun out of these projects where

680
00:51:14.639 --> 00:51:20.079
developers and technical people from multiple organizations
would get together and try to solve these

681
00:51:20.079 --> 00:51:23.360
really big problems. Where does load
balancing come into play? First and foremost

682
00:51:23.480 --> 00:51:29.000
back in that day, think about
Black Friday. So in the early days

683
00:51:29.119 --> 00:51:32.199
of the interwebs, when days like
Black Friday came round, well, you

684
00:51:32.239 --> 00:51:37.280
can rest assured that lots of companies
were trying to figure out how can we

685
00:51:37.320 --> 00:51:42.320
sell as much product as possible online
during that one particular weekend, the Black

686
00:51:42.320 --> 00:51:45.800
Friday, Saturday and Sunday after Thanksgiving, And so a lot of effort was

687
00:51:45.840 --> 00:51:50.280
put into that and as a result, we learned a whole heck of a

688
00:51:50.280 --> 00:51:53.320
lot about how to do load balancing. And what we're seeing now with something

689
00:51:53.400 --> 00:52:00.480
like Kuberneties is that you can do
load balancing and a very deep architectural level,

690
00:52:00.920 --> 00:52:02.719
and that's where you want to do
stuff like that, something like a

691
00:52:02.800 --> 00:52:09.199
load balance for heavy traffic, hitting
a website, hitting a database of products,

692
00:52:09.280 --> 00:52:13.920
hitting your transactional system to get stuff
done. Boy, if you can

693
00:52:14.000 --> 00:52:17.079
solve that all the way at the
bottom, at the foundational level, you've

694
00:52:17.119 --> 00:52:22.119
done yourself a very significant service.
That's just the What did I get up

695
00:52:22.119 --> 00:52:24.800
to? Three out of ten?
Self healing, rolling updates and rollbacks,

696
00:52:24.880 --> 00:52:29.840
resource efficiency, multi cloud deployments.
The list goes on and on and on,

697
00:52:29.920 --> 00:52:32.920
folks, But this does include our
live broadcast and a podcast on a

698
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second coming out that legacy ten fifty
AM, Southern California, NBC Radio News

699
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dot Com. This segment sponsored by
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700
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been providing kindling for a major roast. It'll air in September here in two

739
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thousand and eleven. But no,
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welcome the rock star from Mars,
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y'all merle here. Good news for
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it apparently at KCA radio dot com. So AnyWho listen to the water zone

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fifty AM, the station that needs
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786
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I'm Chris Garagi. One GOP presidential
candidate is promising to pardon former President

787
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Trump. Wall Another beliefs the former
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788
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four race. Republican presidential hope