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Eric Kavanaugh. Yes, all right, ladies and gentlemen, Hello, and
welcome backwards again to the only coast that ghost radio show in the US of
A that's all about the information economy. It's Inside Analysis her. It's truly
Eric Kavanaugh here, and boy am I excited to dive into the world of
continuous delivery CD. You may have heard about ci CD, continuous integrations,
continuous deployments, I should say, And we have with us today an expert
in that space, where we're talking to Adam Frank from a company called Armory,
all about continuous deployment, what that means, why it matters, how
it relates to get hub for example, and get lab and really how development
is changing. You know, I'm an old timer, so I remember the
waterfall approach which took a long time, and it took in months to years
to deploy something. And that model is just broken these days, so you
cannot compete on the stage, especially in the cloud, with that kind of
model anymore. You won't be able to compete for very long. So things
are changing, and Kubernetis is a big part of that. All the observe
ability vendors we've seen spring out of that environment is a big part of that.
I just actually read some slides for a webinar will do tomorrow with merv
Adrian, the IT analysts from Gardner. He's super, super smart guy.
He had this line near the end of his deck that said, used to
take a village, now it takes an ecosystem, right, So things are
changing. Now you can just go with get lab or get hub. But
probably if you want to do continuous deployment, you're going to want to partner
with someone, So it might be Armory. There are other firms. Circle
cis one. I know of some others out there as well, and of
course they all specialize in a certain way. But with that, let's bring
in our friend Adam Frank from Armory. Adam, tell us a bit about
yourself and while you're now focused quite keenly on continuous deployment, Thanks for having
me, Eric, Yeah, so continuous deployment is really at the heart of
you know, where I'm at and where Armory is at as a company.
And my story actually traces all the way back to childhood. I won't tell
you my whole childhood story, but the summary of it is really my father
was part of a development team, and you know, they would get paid
late at nights when things would go wrong, and then of course it would
take a long time to figure out which change was it, how did that
change get out there? And when you start to look at automating that deployment
process and making it safe, making it reliable, you can really speed up
your time to market, speed up your innovation, make sure that you have
the agility to make the moves that you need to make, and just generally
get more time and back in your and stay asleep at night as supposed to
change well, and you just get a real big issue here, which is
trouble shooting. That's one of the biggest and hardest issues that developers space,
I think is debugging code. One of my best friends is a decades long
developers like I don't want to debug someone else's code, right because it's even
harder because you don't even know what they wrote or how they wrote it,
or what they're trying to accomplish. And typically developers don't like to spend time
writing down what they've done, right. Documentation is not fun, which is
why you're seeing a lot of that automated now. And as I think about
what's happening here, I think the key is that we've figured out better and
better ways to facilitate development process and to point out errors. That's I mean
even simple things, for example, like an error on your screen when you
haven't closed a hashtag for example, or you haven't closed a bracket. I
should say to say, hey, you've got to close this bracket. That's
very simple stuff. But we're now into fairly complex stuff that could be automated
and ascertained early that something is wrong. Correct. Yeah, absolutely, I
mean that code that they are writing, those those errors that they need early
on and upfront. Once that artifact has been created from the CI process,
that needs to then get deployed to a number of environments. And we we've
got customers and there's companies out there that are deploying code around the globe,
many different geographies, many different companies, countries, many different demographics. So
there is a lot of process and a lot of complexity that goes into even
small companies sometimes, and that has to be reliable, that has to be
safe, that has to work. Yeah, well, you know, you
were kind of reminding me. For folks who are not developers, it's kind
of like having Grammarly or a spell check in your word processor. It's going
to throw up flags at you as you're typing and saying, oh, this
is a misspelled word or is it a misspelled word? That's kind of what
we're talking about, right, aid, Well, of course, get help
now as copilot, which will suggest things for you, just like Gmail.
I noticed a couple of years ago started on this track and this might have
been a large language model underneath. I'm not sure, but you would be
typing an email in Gmail, like in the actual web client, and it
would complete sentences for you when you could just choose to accept what it written,
or you could type it out to yourself. For me, I'm a
typist, so it's just as easy for me to type the letter as before.
You know, just going to okay, accept this thing. But it's
similar to that, right, It's similar in that you've got some sort of
guardrails as you're developing to help you avoid making mistakes or solve problems before they
become significant, right, Yeah, Yeah, absolutely. I also like to
make the generalization of the manufacturing manufacturing industry and how they've automated so much of
their process to remove some of that human error and make sure that the quality
is there, the quality checks are there, so that something is consistently coming
out at the end of that process every single time. There's there's really no
different here. There's a lot of like I said, there's a lot of
complexity. There's a lot of different geographical compliance requirements and things like that.
So it's really about orchestrating that artifact that has been produced by CEI, tool
that code and deploying it out across these different environments. You've got to hook
into things like security scanners, make sure that code is secure signed, it
is the code that you said is going to be deployed. You've got to
run integration tests on it, looking at your observability data, making sure that
the baseline of that customer experience is still within the acceptable baseline. As you
roll out to more and more traffic, more and more users within these different
regions and around the different environments that you are deploying to. At any point
in time, if any of those fail or if it comes out of bounds
from that accepted user experience, you've got to stop that deployment. You got
to potentially roll back and then figure out what happened, why it happened,
and leveraging some of the error messaging like you said before, Yeah, cluse
those hints to make sure that you can move forward quickly. Well, And
so that's an excellent analogy, by the way, I'm going to remember that
when I'm using myself, because you're right, manufacturing, you have all these
processes that lead up to the production line and all that stuff has to be
correct, and then on the production line it has to be correct as well.
And you don't want to wait until ten cars have come off the line
to figure out that they don't have stereos in them or that there's some part
missing. Right, So you have these checklist chip automated processes making sure that
it's all going as it's supposed to go. And you know, I'm thinking
to myself, the development world has become so complex. I mean, we've
had integrated development environments for decades now. I'd mentioned to you before the show,
the Eclipse Foundation that was one of these IDEs, and you're seeing a
real maturation in that space right now. And the idea is to facilitate productivity,
so to identify issues, errors, bugs before they get too far down
the line, because once it's in production now you've got a whole separate mess
to solve. And so what you're really trying to do is optimize the productivity
of the developers and watch out for any coding errors, any security flaws,
things of this nature as much as you can check as possible before you go
over that wall into deployment. You want to be able to handle all that
stuff as dynamically and automated a process as possible, right exactly. And then
likewise with the deployment process, that you want that as automated as possible as
well. And that ten cars coming off the line missing the steering wheel,
we can relate that back to the waterfall approach that you talked about earlier.
Today's world, you want to tighten that feedback loop. You want to start
to get feedback from your users. Are they using the feature, how are
they using the feature, which way should be lean more into should we be
moved the button from the left to the right as an example, it was
something very simplistic. But you want to continue to build builds things out as
you go to make sure that you are building the right things and you are
staying competitive and really focusing on your competitive advantage. Yeah, and you know
you'd mentioned before the show site reliability engineers. Right at the sr S,
there are the folks that are sitting out there trying to make sure that the
site is working properly, that your website is working properly, the functionality of
the y all that kind of fun stuff, and that's a huge part of
the process. And you're really trying to enable those people to be able to
get two problems quickly or ideally identify them before they go out the door.
And you know, I know one of the tactics used by large organizations is
to deploy an in tiny little increment, so to deploy to one percent,
for example, of your traffic on a particular website and kind of see is
that something you guys recommend as well, the sort of incremental deployment test one
percent of the browsers and if there are no problems up to ten percent of
there's no problems over the fifty percent. Is that's something you guys do,
Yeah, most definitely. We integrate and we support a very variety of service
meshes. When you are leveraging a service mesh, you can control down to
that one percent increment, like you said, so starting out at that one
percent, increasing it to five, ten, twenty five, fifty, and
as you increase, that's automated as well. So it's looking at your observability
data and the baseline of that data, keeping things within that baseline. If
everything continues to be okay, it continues to do log checks, there's no
error messages, and it will automatically increase that increment of that traffic. So
your deployment is saling more and more and more until it's eventually done and your
fully in production and all your users are leveraging it. And how do you
deploy via service mesh? This is one of these relatively new concepts. Of
course, we're also up data mesh on the other side and the data side,
but a service mesh, what do you need to deploy in order to
do that? How does that actually happen? So our solution integrates directly with
that service mash. It integrates directly with your cisoole and it integrates directly with
your observability data. So what we basically do is we have a connection with
your Kubernet's cluster and we would then take that artifact from the CEI solution and
as your SLASH is integrated within of your Kuberneties clusters for your traffic, we
would have that communication to or from every single Kuberneties cluster then all those Kuberneties
clusters and start to leverage that service mess as the traffic control. Really so
we would start to increase it, like I said, one five percent,
ten percent, more and more and more, either focal focal point on a
single environment and then moving on next environment, or doing multiple environments at once
and then increasing to the next set of environments. Yeah, that does is
fascinating stuff. And you know, Kubernetes really did revolutionize it did enterprise software,
right. I mean, they've basically won from what I can see.
There are other ways that you can do it. There's a joke about why
Docker didn't win. I'd be curious to hear your thoughts on this. One
of the theories I heard as to why Kuberneties, one which of course uses
Docker. Someone told me is because developers don't want to pay for anything,
and doctor was trying to charge developers. What have you heard on that?
I mean, there is there is a number of conspiracy theories out there.
I think, you know, coming out of Google, there's certainly a number
of surrounding Google being one of the largest technology companies on the planet as well,
and you know they're really wanting to be in control of all of that
orchestration in that But like I said, those are rumors and conspiracy theories,
so one may never know. But I think as a as an orchestration layer,
it is. It is certainly taken over the globe and really relolutionized the
way that we we do build software and manage software. Yeah. Well,
and it also created the whole observability eCos it seems to me, because even
though it's extremely good at container orchestration, it raised all sorts of other issues
around security in particular and governance. And that's why you have all these observability
vendors right to track to pull data from these clusters and analyze it, companies
like data Dog and New Relic, and it's just a whole host of them
out there, but to be able to analyze what's happening and then give visualization
and representation to the developer group of what's actually going on and how they can
fix things right. And then you look for the pods that have installed,
for example, and just kill those pods and keep the ball rolling. Is
that about right? Is that why we're seeing so much in the observa ability
spaces because Kuberneties is so complex and spins out so much data, so much
data, it's so complex. But also we're in the world where we need
to be proactive. Now, being reactive is going to be a detriment to
that competitive advantage. I mean, it's so easy for you and I to
be using one service one day and have a bad experience with it and then
download another app or use another service that's providing us a better experience. It's
just so easy for consumers and customers to switch from one to the other these
days that you have to maintain that competitive edge. So observability really allows you
to observe the internals of the systems, so you can understand what is happening
to a lot deeper of a lot deeper analysis and you can make decisions into
that. Again, back to the development process, even when you are developing
that code, you can start to understand what users are doing a lot more
and being a lot more proactive about it. Monitoring absolutely still exists. You
still have to monitor your observability data. There's still a reactiveness that happens in
software's. Things do break from time to time. It's really about being proactive
and understanding the experience that you are building as you are building it, as
opposed to supposed to be reactive. I think the other thing that that you
know, we can talk about with toobreadies as well as tooberdies goes hand in
hand with being declarative. You know, that kind of opened up a whole
other world of developer simplicity as well as opposed to being imperative and stating exactly
how you want every little piece of something to happen. Right, it's now
brought into the declarative nature of things that you can state the outcome and that
engine, that declarative engine will start to figure it out for you. Yeah,
and that just blows my mind, by the way. I'd like to
get into that a little bit here, declarative versus imperative. Like it said,
imperative is when you just write out the code, specifically telling a system
what to do. Declarative is when you declare the end state that you want
the system to find, and then it just goes about coming up with ways
to do things. So it's very good at making sure things get done.
I have to believe this is a bit of a performance tax, and they're
somewhere. I mean, if you really understand what you're trying to do imperative
it could still make sense. But in these highly complex environments, I guess
that's why imperative is so valuable, because it can sort through all the different
possibilities of getting something done and then of course test to see how fast it's
happening and thus make optimized decisions. Is that right? Yeah. Imperative is
incredibly flexible. I mean, it gives you a lot of control because you
can, like I said, control every single stage within that pipeline, explaining
exactly how you want things to happen. But the ease of use is certainly
with the declarative nature of this, Especially with the declarative orchestration model, you
cannot, like you said, state the outcome that you would like in this
example, the deployment strategy. You want to use a canary strategy that's going
to take your observability data. You want it deployed out to these five environments
in a way it goes. It starts to figure out the best path for
that deployment, starts to increase that traffic to all of that stuff automatically.
You've just stated the outcome as opposed to stating how you want everything done.
A colleague of actually has a fantastic talk that relates imperative declarative to a grill
chee sandwich. An imperative being explaining exactly how you want this girl cheese sandwich,
made, the ingredients, the process, everything, as opposed to declarative
just stating I'd like a grill cheese sandwich please, and you sandwich. Yeah,
that really is amazing stuff. I'd like to get deeper into that exactly
how it works, but the point is it works right. It reminds me
of one of the funniest quotes I ever heard was my buddy Nikolai Menshikov,
a Russian who told me many years ago, back in two thousand. In
fact, he says to me, the funny thing about the web is that
no one knows how it works. All we know is it works, and
so you just kind of go from there, like testing things just in terms
of how everything connects and the flow occurs. It's like, whoa, it
gets the job done somehow, some way, but folks don't touch that.
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back to Inside Analysis. Here's your host, Eric Tavanaugh. Alright, folks,
back here on Inside Analysis, talking all about continuous deployment in the software
world. We were just talking a moment ago with our friend here, Adam
Frank, from a company called Armory, and they specialize in continuous deployment.
And this is a hot topic these days because you have to be constantly watching
what's happening on your sites in your business. You have to be able to
understand what's going on and then be able to take action. And I think
the demand for agility is what has really fed a lot of this innovation.
What do you think Adam. Yeah, absolutely, and people need to focus
on those competitive advantages that we were talking about and really focus a lot of
their business and their resources on their competitive advantage and staying asile, making sure
that you can close that feedback loop on your stand, your market, your
users, even where you're going next, is just vital to success and staying
in live and thriving in this world. Though. Yeah. Well, and
you know, when you talk about container orchestration, which of course is what
Kubernetes does, I think one good analogy would be that in the old days,
your website could crash and it just goes down. But if you have
one of these new environments where it's underpinned by kuberneties instead of a traditional model,
you may get bits and pieces that aren't working, and like maybe the
search won't work, or maybe a certain button won't load, but overall the
site keeps going. And I think that's one of the benefits, right,
is that even if something does break, it's going to be one piece instead
of the whole thing crashing and burning. Is that right? Yeah? Absolutely,
microservice versus monolith, Yeah, precisely. And I'm actually I don't know
about you, but I've certainly run into that experience where I've been on a
website, I've been using a service and everything's fine until you know your start
rage clicking that checkout buttons and one of the pieces and working and rage clicking
that's funny, that's a good band. Rage clicking, I go see them,
but yeah, so and then you know, in terms of managing the
whole process over time, I'm guessing that armory and I'm sure some of these
other technologies are capturing a lot of information about your environment right and what works
and troubleshooting and when you find something that goes wrong being able to identify that.
I mean, I've seen in some of the newer plays around service management,
basically, like the stuff that Service Now does and these guys is you
will have a bit of a playbook generated for the SRIS that says, Okay,
if this happens, check this, check this, check that based upon
things that have happened in the recent past, or over the past year,
or whatever the case may be. Because you never know, like there's always
some question as to what exactly is going on. But if the sres have
a playbook to look to that gets fed by these algorithms, and human beings
and acting with it. That's your sort of go to strategy. Is that
about right? I think a lot of people are certainly leveraging strategy like that.
As a continuous deployment company, we're very keen on looking at that and
integrating with some of that data that does say something is going wrong here,
something is not right, whether it be checking logs, whether it be looking
at metric data and baselining that, we certainly want to take a look at
that data during the deployment. As traffic is increasing, as we're deploying from
one environment to the next environment and increasing global span of that deployment, we
absolutely want to take a look at that data and have it automatically analyzed using
statistical analysis and other methods to make sure that everything is going to be safe
and sound with that deployment. Yeah. Now, in terms of market factors
like the cloud, cloud computing fundamentally changing everything that things are changing all the
time. Microsoft just announced Microsoft Fabric, which sounds pretty interesting. AWS is
always changing things, always coming up with new services, etc. And there's
this whole move to the cloud from on prem. Do you deploy primarily on
prem or can you actually be used in an environment to facilitate a move to
the cloud where and when that's appropriate. We deployed on prem we deployed the
clouds. It's really about having a mantra that we believe in and meeting our
customers where they are. And I think a lot of people these days are
really going with a hybrid strategy that they've got a number of things that are
on prem while they've got a number of things that are in the cloud as
well. Certainly, some companies that have been around much much longer, they
have to evaluate the return on investment for taking something that's on prem and you
know, we are architecting it for the cloud, or even perhaps we are
taking it to be cloud native. You know, one of our customers,
you know, it's a forty year old plus customer and you know, with
their core piece of software. We sat on with them, we talked about,
you, what's the likelihood that you would ever do something like this and
move to the cloud and cloud data, and it's like, well, we
whipped the return of investagers wouldn't be there. Where's millions of lines of code
here and it's a forty year old plus company. By the time we did
this maybe forty years in the future from now, and we'd be talking about
the exact same thing all over again. That's pretty funny. Yeah, I
mean, you know, and that's the sort of monolithic approach. You know,
when I look at the broader market, you look at an SAP for
example, which is a monolithic approach. Now, I've been reading that they're
getting a bit more friendly with Kubernetes deployments. They're looking at ways to break
all that stuff down. But that's a very difficult thing to break down,
right. The larger it gets, the harder it is to kind of sort
of deconstruct and then reconstruct. So to me, I think cloud is going
to be largely net new use cases or new ways of doing old things.
But you know, in terms of taking on prem code and trying to port
it into the cloud, that's a very tricky situation. It's really not typically
a good idea, right, Yeah, and again it has to be evaluated,
and you know, sometimes that evaluation may turn out that, yeah,
it's a good idea, I think, you know a lot of the times
that we've seen you know, it may it may not be. So you
know, we really want to be meeting our customers where they are and deploying
both on prems, deploying to the cloud with kubernet is really being at the
forefront right now. But even everybody that's leveraging Kubernetes that we talked to as
well, still has some bit of servilists or other components within their stack that
isn't Kuberneties. Some of them might be migrating it to Kubernetti, some of
them might have a servialist component for very very valid reasons that they want to
grow as well. So being able to deploy the servilest, being able to
deploy the Kuberneties, you know, these are all very very important things for
their developers and for our development community to maintain their agility and increase their innovation
and time to market. So right, and then AI ops is something else
you folks are talking about. And now thanks to these large language models like
chat, GPT, all of a sudden, the entire world has figured out
that AI is real. Now, it wasn't the first AI solution, but
it's certainly the most broadly appreciated in terms of its impact and its value.
Now there are at some gotchas in there. These large language models will hallucinate
as the term it gets used. I think that's pretty funny, and others
they just make stuff up right there, grabic it's a basic it's a predictive
engine for language. So you prompt it and then it will give you an
answer based upon what it thinks. You want to see that from its huge
troves of information. But there's a bit of a black box issue going on,
and with artificial intelligence, we want to know, you want explainability.
Where do you fit into the AI ops equation? Is it that you're bringing
that regimented approach of deployment to the rather unwieldy world of AI and that's how
you can kind of an gate some of these problems or what's your story there?
I think deployment has really evolved from the imperative nature is kind of where
it started building out pipelines, building out every single stage of that pipeline,
to now the declarative nature that Kupernetes has brought into the world. We are
very much focused on delivering a pop shelf experience that is declarative, kind of
that get ups term that it's been thrown around and coined by another company,
making sure that our users can declare their outcome that they want and that's what
they will get with that, so they can really focus on writing code as
a point of figuring figuring out how to deploy that code. But I think
generative AI certainly has the potential to be that next evolution from declarative. Like
you said, there's a lot of explainability and things like that that people do
want. But we've certainly been playing around with generative AI in the deployment space
and we're able to produce some pretty cool configuration and it's pretty cool artifacts well.
So this is getting really interesting from my perspective because when you start thinking
about the different ways that you can deploy software. You've mentioned somebody's paradigms already,
server lists versus Kubanetti's traditional monolithic architectures. There are lots of ways you
can get things done, and there are also lots of ways you can optimize
things. So if you look at what VMware did to traditional computing, while
they really took this amazing new novel approach to optimize the use of hardware,
right, so what they were doing communities is not the same, but it's
sort of the same objective that you're trying to accomplish. But when I think
about the different deployment options that you have and where functionality actually gets done,
where functionality is done versus where data is delivered, for example, it's a
tremendously wide tapestry of how you can accomplish some stuff. What are your thoughts
about that, and what are your thoughts about trying to explain to people why
it matters and what benefit you can get by using these technologies in terms of
optimizing productivity, reducing risk, all these kind of things. How do you
explain that to people? Because it is very complex, But it's just it's
a question of different layers in the architecture and what gets done at this layer.
Because it used to be just the OS, right you had your operating
system on your computer and you had to update your operating system and you had
apps that it had be written for that OS, etc. Well, now
we have kubinelities, we have serverists, we have other ways of doing things.
Still stuff is getting done, code is getting processed, etc. But
a question becomes where in the sort of workflow do you execute this functionality versus
that functionality? Where do you pull in data solve it that way? I
mean, there's an infinite number of possibilities, but how can you make sense
of all that for an enterprise deployment for say like an insurance company for example.
Can you give us to some color or context about how you would explain
to a client the different ways you can do things and how you can optimize
what they're doing. Yeah, I think we would really start by talking about
their developers and their developer experience that they have today. How much do the
developers have to know, how much the developers have to get their hands in
there, how much time are the developers taken away from writing the code that
they love writing and innovating as opposed to figuring out all of those different layers.
We just talked about the infrastructure and like how to actually deploy their software,
So we talked about it in terms of abstraction. So when we look
at different layers, we want to talk about the abstraction that's put on top
of that layer to really make the developers lie easier and have a fantastic developer
experience. And that's really where the declared of orchestration all that we delivered has
come in, so being able to take the outcome that they want really provides
that layer of abstraction on top of their Kuberneties, on top of their servi
lists, so they can write their code. They can have the CI system
produce the artifact, and then that artifact gets deployed out to the different infrastructure
that's around there. Now, typically they would have a platform team. That
platform team might be involved with all of the infrastructure or certain elements of the
infrastructure. They might have different infrastructure teams. That's kind of specialize in that.
But we really want them to focus on writing that code and having that
layer of abstraction just to make that nice and easy. Yeah, that's interesting.
This is just fascinating stuff. So to your point, there are tons
of different ways that you can do development. There are tons of different ways
to build your teams to orchestrate things, which tools and technologies you use,
which languages you use for crying out loud. And now at these large language
models, well guess what they can write code too? Right? That was
my first thought as like, wait a minute, if this can write in
English, I'll bet it can write in code. Yes it can. Oh
that's an interesting factor too, right, because now business people could say,
give me some coball code that would process transactions and send a report to this
thing over here, and they'll go and give you a basic set of code.
Now that does need to be vetted. I've heard from a lot of
people that it'll get things close but not exactly right, and so you do
have to do some finishing touches. But nonetheless, that's a big change in
the marketplace, right to have any number of people able to just kind of
spin up big bits of code. Are you seeing that yet in the marketplace
or is that still a bit far off? I think it's still a bit
far off. There's certainly a number of people that are playing with it and
producing it. Like one of the areas that we as a company have found
a good use of it. We are also a global company. English is
not the first language for a number of our employees, so the ability to
produce comments around some of that code has been fantastic. You know. It's
really produced with interesting, great English around that code and made it really easy
for some of our developers where English is not there language. So that's that's
kind of where we found a fantastic you so far, but certainly playing with
producing bits of code, Like you said, it's it's absolutely going to be
vetted. And how that code all comes together to produce a feature, to
produce a product. I mean, that's a much much larger topic, and
yeah, certainly get into it. But well, I was just reading Aligadzi
from Databox was writing the day use cases for LMS for large language models,
and he said one of them is just a Basically I helped ask right to
just explain to you what's going on now. I think we are going to
see these single tenant versions come out where you'll be able to have your own
LM for your enterprise and plug it into your system. So it's not just
connecting to the world because there are some security issues and privacy issues, etc.
But nonetheless, what kind of gets me. Maybe it's my last question
for you is how good do you think these large language models will be at
understanding steady and configurations and thus give feedback to developers about how to configure these
new environments. What do you think about that. I think they will be
I think I think people will put the time and effort into training them.
I think people will also put the time and effort into understanding how to write
proper prompts and think that that will actually that will produce pretty good quote,
pretty good, pretty good configuration and things like that. So I think they
will, especially within smaller environments where like you said, they're isolated and you
know, has to do with that particular business, there's less, less training,
less kind of pathways that can deviate there. So I think they will
certainly to do well. This is super cool stuff. Will folks who've been
talking to Adam Frank in a very candid conversation about the modern world of continuous
deployment, Look these folks up online. Armory is the company and he's on
LinkedIn. Adam Frank will be right back. You're listening to Inside it Out.
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Welcome back to Inside Analysis. Here's your host, Eric Kavanaugh. All right,
pok back upon himsialysis. Who's Aaron Kavanaugh Here. We'll be talking all
about software deployment, enterprise software design and deployment, continuous integration, continuous delivery
with our friends from Armory. And for the next couple of segments, I
wanted to die into the whole topic of Kubernetes and modern software development. What
has changed, why it's changing, how that affects you as an end user,
how it affects you as a business person, how it affects you as
a potential coder out there in the marketplace. Here in Pennsylvania, there was
the Xcel pipeline was killed a while ago, and some people were, i
think ironically joking, oh, learn to code, suggesting that the folks who
were going to work on that project could just go learn to code. Well,
the truth is these days they probably could. Right. First of all,
anyone can code. It's it's like writing. Most people know how to
write in a language, so you can learn how to write encode. It
seems very daunting at first, perhaps, and certainly some of the more advanced
work that gets done these days in artificial intelligence and machine learning, even just
setting up infrastructure as code as they call it for the business applications that are
running today. All that stuff can get very complex, but that doesn't mean
you can't learn to code. Any One can, frankly, if they have
time and effort in the interest level. It takes commitment, It does take
some dedication, It takes patience, I think more than anything, it takes
an engineering mindset to tackle some of these challenges to figure out how you're going
to be able to struggle through it, so to speak. But we're going
to talk a little bit about large language models. So of course chat GBT
is taking the industry by storm. Dolly two point zero came out from Data
Bricks. There are several other of these large language models out there, and
they're very interesting things. So they are text engines, text generative engines,
if you will. There's a term generative AI that gets thrown around a bit
on fact yours truly and doctor Robin Blore. We're working with a company called
Praxi Data right now, very interesting company. P r a Xi Data will
have a paper coming out on this very topic on the challenges of generative AI.
I'm joking. The titles ship be generative ais that a promise or a
threat, going back to our childhood days when we would say that as a
taunt to someone where someone was let's say you either promising something sketchy or threatening
something silly, and we joke. Is that a promise or a threat as
a way of sort of diffusing the situation, I guess, or dealing with
it. But there are very many promising use cases around generative AI. And
it can write code. So here's the thing. It doesn't just write poems
and sonnets and research papers and school papers and so forth. It writes actual
code and lots of different languages. It can write, as I understand it,
in Cobal. It can write in Basic, and write in Pythonic and
write in Java. Well what does that mean for the software development industry?
And I also wanted to talk about shift left versus shift right, just so
you have some of these terms in your mind. But let's first talk about
actual code generation. Well, this is some very interesting stuff. So there
are also two main kinds of programming. You heard as talking in the last
couple of segments with Armory about imperative versus declarative. Imperative programming is where you
write out, step by step the specific instructions that you want the application to
do. Declarative is much different than that. Declarative You say what you want
the end result to be and let the engine write the code on the fly
to do the job for you. While these new engines, these new or
large language models are declarative in nature, at least I'm pretty sure they are,
and so they can come up with different answers. You can actually do
it twice in a row and you'll get a different set of code solving something
in a different sort of way. Well, this actually speaks to the I
guess, promise and threat that the blessing and the curse of writing enterprise code
these days are writing code for applications and for systems is that there are so
many ways to get things done. So typically what you have to do us
understand, all right, what is the code based that we're working on at
this company, What are our developers like, what tools do they like to
use, what licenses do we have? There are lots of different ways you
can do things. We were talking about integrated development environments IDEs. The Eclipse
Foundation, for example, has been around for a long time and their vision
was to create a resource for developers to help developers. Now, of course
we have all these engines like GitHub and get lab and others, and with
GitHub you've got this thing called Copilot, and Copilot will just go along with
you and help you write code. So this generative AI thing has really gone
from zero to a thousand miles an hour and almost no time. And you
can tell it's a big deal because all of the software vendors are talking about
it. I mean software AG back in February had just announced the connector to
chat gpt or to the open AI platform, and that was pretty early in
the game. They were one of the first. Now everyone is i mean
not everyone, but Darnira, everyone is talking about ways to connect generative AI
engines to their software applications, to their technologies. Well why is that.
It's because it's a really big deal and it can do lots of interesting things.
It can do a contextual analysis, it can refine the creative content that
it generates, it can do a lot of different things. And there are
open APIs coming up on a regular basis to this system for ways to leverage
its power. Even more so, we're really kind of just at the beginning
of these large language models, of these foundational models, if you will,
And what that means from a coding perspective for you as a possible coder,
is that you can get going right away. You can have chat GPT create
code for you and then have it explained to you what the code is doing.
You can have a try around different tests and different mechanisms of doing something
like a data quality filter, for example, for your database. You can
get really specific with these technologies and it'll hack out code. Now, what
I've been told by quite a few people now who are doing this is that
you do have to do some fine tuning. So right out of the gate
it's not necessarily going to be perfect, but at least it will be close.
And so by giving you something to work with, it's really expediting the
process. Well guess what, that's the same thing it does in the world
of content creation for marketers and for content people. Chat GPT can be very
useful for generating ideas for tweets, for example, because you can give the
engine and you use a prompt is how they describe it. It's kind of
like Google. You click into the Google prompt and you are type in your
words and you get a search query. Well, with chat GPT, you
put in your instructions because these are instruction following models. Basically you put in
your instructions and then it comes back to you with an answer if you will
to your request. So you can write ten tweets about the power of Kubernetes
to help business people. And I actually just did this before doing this show,
just to see how well it does. And it does very very well.
So I prompted at saying kubernet is whereas one of the top ten most
important benefits of kubernets for developers and for businesses, and within a couple of
moments it banged out this top ten list for developers. Scalability. Kubernatis endables
developers to easily scale their applications horizontally by adding or removing containers, ensuring optimal
performance and resource utilization. Two automated deployments, three, service discovery and load
balancing. I mean load balancing in and of itself has been an area of
expertise and of study for since the computer came out. Load balancing is all
about, literally what it sounds like, balancing the load, the workload that's
coming in that we have to handle. How can we balance it? Well,
you know, you go back for some ancient history here to like the
year nineteen ninety eight, nineteen ninety nine into two thousand, there were groups
of folks at companies like Yahoo for example, who were working intently and in
fact, a lot of the open source movements spun out of these projects where
developers and technical people from multiple organizations would get together and try to solve these
really big problems. Where does load balancing come into play? First and foremost
back in that day, think about Black Friday. So in the early days
of the interwebs, when days like Black Friday came round, well, you
can rest assured that lots of companies were trying to figure out how can we
sell as much product as possible online during that one particular weekend, the Black
Friday, Saturday and Sunday after Thanksgiving, And so a lot of effort was
put into that and as a result, we learned a whole heck of a
lot about how to do load balancing. And what we're seeing now with something
like Kuberneties is that you can do load balancing and a very deep architectural level,
and that's where you want to do stuff like that, something like a
load balance for heavy traffic, hitting a website, hitting a database of products,
hitting your transactional system to get stuff done. Boy, if you can
solve that all the way at the bottom, at the foundational level, you've
done yourself a very significant service. That's just the What did I get up
to? Three out of ten? Self healing, rolling updates and rollbacks,
resource efficiency, multi cloud deployments. The list goes on and on and on,
folks, But this does include our live broadcast and a podcast on a
second coming out that legacy ten fifty AM, Southern California, NBC Radio News
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