Narrator: You're listening to the humans of DevOps podcast, a

podcast focused on advancing the humans of DevOps through skills,

knowledge, ideas and learning, or the SK il framework.

Richard Whitehead: Right, so the good news is people's jobs like

most definitely not at risk. The problem we're trying to solve or

solve, I think is it's increasing at a greater rate

than I think we can. We can sort of solve the problem. So the

expanding nature of the problem, I think, secures people's

employment for a very, very long time. I think in every aspect

of, of any form of digital transformation, when you look at

any aspect of the business, it doesn't matter how much effort

how much code rewrites how much automation we do. The

opportunity to refer to this as an opportunity, not a problem.

The opportunity is increasing. So, so fast, that I don't think

anybody's going to be able to jump anytime soon.

Jason Baum: Hey, everyone, welcome back. It's Jason Baum,

Director of Member experience at DevOps Institute. And this is

the humans of DevOps podcast. I hope you had a great week. We're

glad you came back to join us. So let me take you back to the

80s The machines are coming to get us scenes in lines from the

Terminator and it's many sequels are forever etched in our

brains. It hauntingly depicts a world hell bent on technological

growth that led to the rise in advanced machine learning

techniques and artificial intelligence that would

ultimately lead to the world's demise. Well, we're on our way

there, folks. But instead of doomsday scenarios, we're all

picturing. Ai ops has emerged as an essential step forward for

enterprises in a variety of different industries. Gartner

defines AI ops as artificial intelligence for IT operations,

which combines big data and machine learning to automate it

operations processes. At its core, it's all about it, teams

and organizations can use AI to manage data in their

environments. Through this approach, teams can employ large

scale datasets, machine learning and automation to MAKE IT ops

faster, simpler and more efficient. Many believe AI will

not only impact organizations, but will become a major facet in

our everyday lives through the emergence of new applications. I

mean, we already see ML and AI every day with things like face

ID smart replies, product recommendations, chat bots, you

name it. Today, we're going to talk to Richard Whitehead

evangelist and chief CTO of Moog soft about what aiops really

means for humans, as opposed to being stuck in the 80s version

of this story. Sorry about that. Richard, are he ready to get

human?

Richard Whitehead: How suddenly?

Jason Baum: First of all, thanks so much for coming on.

appreciate having you here on the podcast. And my apologies

for taking it back to the 80s. But every time I talk about AI,

my mind immediately goes to the machines are coming for us.

Richard Whitehead: And certainly, when I started

working in this space, I think every single PowerPoint

presentation I saw both internally and externally had at

least some reference to Skynet and a terminator. So yes. It's

not that not that far back.

Jason Baum: Yeah, no, it's not that far back. And I think

people are still still scared by it. I have a funny story that

I've told. Perhaps in the past, I'm trying to remember maybe

maybe not, of my mother actually keeps her Amazon name cannot be

said because it's actually in the room with me and we'll start

speaking. But we know what we're talking about. She puts it away.

She actually keeps it in a cabinet and when she wants to

use it, takes it out and plugs it in and then says the magic

words to turn it on because she's afraid of it listening to

her.

Richard Whitehead: That might be a legitimate fear. It's always

listening that it has to but yeah, I think I you know, I'm a

pragmatist. I don't fear artificial intelligence, but I

do occasionally have a sense of disappointment. You know, when

my door camera notifies me that there's a person outside and

it's it's clearly a dog. And, and also some, some shopping

recommendations. I get, you know, I I, if I were to buy an

item such as a dishwasher, I don't feel the need to be to

have somebody suggest that I buy a dishwasher for the next six

months. So I think people, you know, it's less fear, it's more

disappointment in what, what AI can bring to you based on some

of the some of the more obvious and commercial barons that are

out there.

Jason Baum: Yeah, so let's talk about what AI is. So I feel like

that's a good place to start. You heard the Gartner

definition, want to include that because I always feel like

having an academic of sorts definition is important to hear.

I've also heard someone very succinctly put it that AI,

artificial intelligence is simply the problem. And then

once it's solved, it's no longer AI, which I think is a

fascinating way to look at it. I'm curious how you would define

it.

Richard Whitehead: So much. So I definitions a little broader. To

go back to the specific definition, the garden

definition, which is actually evolved into that definition.

Because when you put the letters AI together, people

automatically assume it means artificial intelligence. So AI

Ops is the application of artificial intelligence

techniques to IT operations. So that's really it. And, of

course, means that it's a very broad definition, which means

there are a lot of technologies and techniques and solutions out

there that all fit into this umbrella definition. So when

people talk about artificial intelligence, there's a general

sense that what you're talking about is technology or

computers, that are in some way attempting to replicate humans,

you know, and that's, that's where the inevitable screenshot

of the Terminator, robot appears, and so forth. And I

think that's, that's generally true. So, you know, when you

think of chess playing, computers and things like that,

and that sense has been largely reinforced by some of the early

adoptions of AI in things like service desks. So you know, when

you call into a service desk, your first interaction is likely

to be with some form of AI capability where it attempts to,

to give you an answer very rapidly without any form of real

human interaction. And, you know, they, in most cases,

they're attempting to sort of pass the Turing test. In other

words, you're talking to a computer and is trying to make

it as human like that interaction to be as human like

as possible. So while it's true for things like service desks,

when you when you dig deeper into some technology, and start

talking about, you know, the concept of sort of monitoring

and observability remediation, and things like that, it becomes

less attempting to replicate a human but more attempting to

replicate what a human would try and do if they were involved. So

it's not a human interaction, it's an application of human

based intelligence, but in an automated fashion. So with that

broad definition, you're incorporating not just sort of

that chess playing type thing, that's actually the least of the

component. But you are talking about things like machine

learning, you're talking about sort of some sophisticated

algorithms that maybe do linear regression, and you're talking

about some techniques that are in the periphery of artificial

intelligence, such as natural language processing, you know,

I've mentioned already mentioned two that I'm personally

relatively familiar with, which is machine learning, and natural

language processing. And these are things that you don't

necessarily think of when you're talking about AI, but that

they're absolutely relevant and very pertinent to solving

specific problems.

Jason Baum: And with that, that's a good lead into this

next question is, what are some examples of, of problems that

we're looking to solve with AI ops and machine learning

specifically? And And also, when you hear about, okay, we're

looking to solve problems, and automate and speed up some

processes. I think a lot of the some of the misconception then

is, well, now I'm going to lose my job. AI is going to replace

me. So perhaps you could address both in this in in your answer,

what's the problem? And then as we solve it with AI ops, how are

we like, are people's jobs at risk?

Richard Whitehead: Right, so the good news is people's jobs are

most definitely not at risk. The problem we're trying to solve

solve, I think, is it's increasing at a greater rate

than I think we can. We can sort of solve the problem. So the

expanding nature of the problem, I think, secures people's

employment for a very, very long time. I think in every aspect

of, of any form of digital transformation, when you look at

any aspect of the business, it doesn't matter how much effort

how much code rewrite how much automation, we do the

opportunity, I want to refer to it as an opportunity, not a

problem, the opportunity is increasing so fast, that I don't

think anybody's going to be out of a job anytime soon. In fact,

I think if you look at some of the roles, the newer roles that

are emerging as a result of digital transformation, such as

site reliability engineers, sort of developers in general and

sort of operations, folks who are emerging in sort of a DevOps

type, type capacity, you know, that's an expanding market

opportunity, not a shrinking one. So individual teams might

be smaller as a result of, of this technology. But the market

opportunity in general is such that I think it's going to give

me a long time before the demand for people in these roles cools

off. So yeah, not not a problem there. We're dealing with a an

exploding market opportunity. So basically, it sort of comes down

to, I think I mentioned it earlier, the notion of

automation. So when we talk about AI replicating human

activity, I tend to think of an in this this sense, when you're

looking at something that a human would do on their date,

day to day, sort of line of work, when you're solving a

problem addressing an incident, debugging something, the

question we always ask ourselves is, what's the most common task,

what's the most common and repetitive tasks that a human

performance, and those are the things that I think are easy

targets for AI, to replicate, because they tend to be the

mundane tasks, you know, we tend tend to refer to them a lot of

toil. The stuff that you do every single time, that doesn't

necessarily add value, but it's just a task that has to be

performed in order to move on to the the next job of of actually

resolving the issue or, or finding the error. And that's

something that I think, is sort of overlooked, people tend to

think of AI as being an end goal, we're going to completely

replace a human, and you throw data at it and you get a

solution at the other end, I tend to think of AI certainly,

we sometimes refer to it at moogsoft as applied AI, it's

basically a very small tool, you can take a very small tool to

achieve a very specific task. And it could be something as

simple as doing a bit of triage, augmenting some information. So

that's one less task you have to do. That's one less system, you

have to log into to get some additional information. If that

information can be gathered for you, and presented to you.

That's one less mundane task you have to perform in order to get

to the really important stuff, which is using your your human

brain to resolve the issue. And so yeah, so applied AI is a good

way of looking at it.

Jason Baum: That's what it's all about. Right? Getting rid of

that mundane. I think that's the goal. Right? Right.

Richard Whitehead: So certainly a DevOps ideal, right? Yeah. The

daily work.

Jason Baum: Yeah. Efficiency. So um, so it's is it is it once

we're, it's set up, right, we've got it, the mundane is gone.

It's working, you know, everything is being automated,

is simply plug and play. And now we just let it go. Or, you know,

can we trust the machines to continue it? And just in

perpetuity, I guess forever? We're learning the machine is

learning and, and everything is all set?

Richard Whitehead: Well, there's a couple of angles to that. The

first one is, can you just unleash the power of AI and have

it do its job? And that the second aspect of that is, you

know, is it a one one time deal? Do you just use it up once and

let it run? So to address the can you unleash it, maybe maybe

one of the talks or one of the challenges that I I've had

dealing with sort of sort of very conservative minded IT

operations folks, when trying to bring in something as fuzzy as

AI, to a previously incredibly deterministic world where

everything is well understood, and every action has a very well

understood reaction. What are the challenges? Well, how can

how trustworthy is it? Is it going to get the same results

each time? And the answer is well, not always, because if the

input data is different, then it might respond differently. Um,

so from a trustworthy standpoint, you have to sort of

take a step back and think, well, there are many different

types of AI technology, even down to something like machine

learning, there's the concept of supervised and unsupervised

machine learning. And so if you're gonna just want to throw

some data at the proper system, and have it do its thing, you're

probably describing unsupervised machine learning. There are

certain techniques or certain areas where that's very

applicable. That's particularly trustworthy. In areas where

there's no real learning that needs to be done. I think a lot

of the concern that people have over sort of machine learning

and AI is, is where training has to occur, and how accurate is

the training, but there are certain techniques that just

work. So you don't need to build a model, you just react to the

data that's coming in. And so an example would be, you have a

flow of data coming into a system, and you're looking at

that data in real time and trying to identify patterns. So

you're not necessarily comparing it to a historical model, you're

just looking at the data as it is, in real time trying to

determine patterns. So that's a good example of unsupervised

data, because there's no training model, you're looking

at the data in real time, and coming up with an answer. So

that's a good example of something where you can just

turn it on and let it do its magic. There are other areas

where, you know, training becomes more of a more of an

important component. And I think, from our standpoint, when

applying those techniques to an operations type environment,

that's where the human becomes important. Because the

supervised model at that point, the training is done by a human.

So the system would say to you, this is something that I

determined from the input data, what do you think, and the human

has the opportunity to train it. So you know, practical turn,

that might be the ability to tag data, or press a button to give

it a thumbs up or a thumbs down. And that sort of human guided

supervised learning. Again, it becomes trustworthy, because the

human has provided the input. It's not something that the

system has determined on its own, that you're actually giving

it some sort of positive affirmation. So if the model is

good, it's because a human has trained it to be good, based on

their current knowledge.

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Jason Baum: Interesting, so as a follow up to that does risk of

getting it wrong, play into the decision of whether the machine

is let to let it go type, like what you're saying just unleash

it, as opposed to a human being being kind of on the other end

sort of helping it does risk play into that of getting it

wrong?

Richard Whitehead: Well, the good news is, in most IT

operations environment, the relative level of the risk is

fairly low. But not in every case, obviously. And that's

where a lot of concern, I think comes I have no idea who coined

the phrase, but I like it, which is new to err is human. To

really mess it up you need a computer. And that's one of the

that's one of the the challenges with with automation, is that

you can really make a problem worse by fully automating some

kind of reaction to it. Risk is certainly an issue. When you

look at some of the stories in the press about artificial

intelligence. Nobody ever really publishes the good stories,

that's just that just happened. That's life. That's we're all

used to that. We take that for granted. It's the negative sides

of AI that get a lot of the publicity. And, you know,

there's a lot of concern about bias in learning models, and,

and some of those sort of issues. And that's really sort

of a big data problem where you're dealing with large

amounts of data from questionable sources that have

been used to train models. And from my standpoint, the way you

mitigate that risk is you move away from third party data. And

you try and focus solely on your environment. So don't use

external training data. And you can do that in an IT operations

environment, it's much easier to do that if you're, you're not

dealing with sort of medical data from the last 10 years,

that may or may not be tainted by some some, some poor poor

quality data that was introduced that you have no control over.

You're dealing with an IT operations environment you're

dealing with, with infrastructure and technology

that's in your control that you have. So you can you can build

models and do training, from data that that high quality data

that you that has good provenance, you know where it

came from. So a lot of those concerns, like I say, that are

based on poor quality models and poor quality data from

questionable sources. The good news is it operations has less

of a concern with that data, because we know where it comes

from.

Jason Baum: So with all of that, and it sounds like there's a lot

of management that has to go on behind the scenes, who's doing

that, who's who's going to manage the solution? How has

like the tech team changed? How has it been the work being

distributed? Where does AI ops play into this now? Do you need

a data scientist? Do existing team members take on new roles?

How are you structuring it?

Richard Whitehead: Right? So yes, we obviously have first

hand experience with that as a technology provider in that

space. And the answer to the Do we need a data scientist, is if

you're going to build a solution yourself, if you're going to

roll your own as it were, then yes, you're going to need a data

scientist we have, we have data scientists on board as part of

our team. They're slightly outside the engineering team.

And just like every other organization, they have

different skills. They come from different backgrounds. The war

science scientists, and they are engineers, the the sort of

programming languages tend to be more Python are focused and so

forth. So different people. Absolutely. If, however, you're

in IT operations, you probably shouldn't necessarily be looking

at getting a data scientist on board. Because there are

technologies out there commercial technologies, open

source technologies, where that work has been done for you. And

I think when people ask me, you know, am I going to have to

retrain my staff? I chuckle and say, No, the impact of AI on

operations is minor, it's almost trivial compared to some of the

seismic shifts we've already seen in the last five to 10

years. As operations people, and we shift from this everything

from to everything is code type environment, we now have

operators who are themselves, they look just like software

engineers, they're conversant in one, two, maybe even three

programming languages. They're fully conversant with code

repositories. And that that shift is far bigger than

anything that the introduction of AI is ever going to change.

So no, you're not going to have to become a data scientist just

to operate this. The technology is going to be in a form that's

easily consumable. It's going to look like software, it's going

to like software, you'll treat it like software, you're not

going to be building models yourself, the technology is

going to be doing that for you. So no, don't think you'll need a

data scientist. But absolutely, you're going to need to have

people who are very consistently conversant with with software

and infrastructure as code and that sort of thing.

Jason Baum: So where does AI ops ml ops? Where does that fit

within DevOps culture?

Richard Whitehead: At the end of the day, it's it's just

technology. It's it's a tool. Okay, so it's, it's neither a

good fit nor bad fit. It's just technology. If good AI ops

technology will fit very well. Because it just looks like

software, it reacts like software, you can configure it

as code. The changes you make are going to be very easy to

work with. The technology will offer both a strong UI but also

strong API's so the technology can fit into and be integrated

into a DevOps tool chain. It's just part of it. Part of the

value stream. It shouldn't stick out as necessarily being

something that's, that's a standalone industry or a

standalone job title, you shouldn't have to hire an AI ops

engineer. It's just, it's just technology.

Jason Baum: So what what are you excited about? With the future

of AI ops? What's what's coming down the pipeline that should

get us all excited?

Richard Whitehead: I think, you know, for me, as somebody who is

involved in the very early stages, just one, the first

thing is the adoption of it. It's the fact that we've made

that shift from this is scary, I don't know if I can trust it to,

gosh, I can't imagine life without it. Do you remember what

it was, like 10 years ago, when we have to do this stuff

ourselves? How How dull and boring was that aiops also

brings some stability. And there's a certain irony to that,

because when we talk about things like the fuzzy logic of

AI, people think of that as being kind of non deterministic

and scary. The reality is, it makes systems much more, much

more robust. So the ability for a system to be able to adapt

means that when you get certain changes, AI adapts along with

it, and becomes very flexible, and means that the sort of the

total cost of ownership, the maintenance of an AI system

drops significantly, because it's adaptive. And that's, I

think, really significant, that that's a, that's another thing

that just improves your sort of daily life is knowing that when

you plug something in, yes, you're going to have to maintain

it. But it's not something that's going to be a full time

job. It's not something that every single day, you're going

to have to touch and tweak. And I think, you know, people,

people forget that when they talk about automation. And you

hear that term, sort of no ops, floating around of like, well,

we just fully automate everything. And that's it that

humans can go on vacation and never touch it again. Well,

life's not like that. One of the benefits, one of the goals, even

of digital transformation is the ability for things to change at

a blistering pace, you want things to be incredibly reactive

and very dynamic. And you throw into that the natural entropy of

any, any system, and changes absolutely guaranteed, and the

rate of change is accelerating. So nothing's ever going to be

installed and forgotten about, this isn't a telecommuter. Most

of us are not dealing with a telecommunications environment,

where you install a switch, and then you'd love it and take care

of it for 25 years. Everything changes dramatically. So having

a system that's at least a little bit adaptive, and doesn't

require, you know, constant attention. You know, that's

something that makes people very, very happy. And I think

that's something I'm, I'm looking forward to, people seem

to benefit from. Also the just generally looking at new

opportunities. So as I mentioned, as we start to deploy

AI ops, in production environments, it's the little

things that are the game changes, the little benefits

that are multiplied over, you know, hundreds of times a week

that make everybody go Yeah, okay, this is really cool. I'm

glad we installed that that made that made a big difference.

expanding that to do some some other intriguing use cases,

finding new cases, new use cases is something I'm really excited

about.

Jason Baum: It sounds like when this is going to when it's when

it you know what's working is when you kind of forgot about

it. All right. Yeah. Right. That's, that's the end goal. So

I look, we're coming up to the end. This is I could talk about

the subject forever. I think it's fascinating. I love hearing

you speak about it. It's, it's, gosh, I can't believe we're

here, right? This point when some of these, these mundane

tasks are just no longer going to be a thing are already not a

thing. So I do like to ask, kind of like, this isn't like a

gotcha question. But but sometimes it is. Today's is not.

I like to ask a thinker. So what's one question you wish I'd

asked you? And how would you have answered it?

Richard Whitehead: Um, just just from sort of a personal point of

view as a tinkerer and an experiment, you know, I wish we

had more time to talk about natural language processing. You

know, I think I've been doing this for a very long time

somebody asked me, How long have you been writing regex Richard

and I it's, it's measured in decades. Um, I think might be

three decades now. And for me, you know, I, I joke that, you

know, I've only been writing regex for 30 years. So I'm a

relative noob I'm still learning. And then along comes

natural language processing. And by, by using sort of NLP, you

can do things in, in a couple of seconds. That would take maybe,

I don't know, 3030 minutes to express as a regular expression.

And, you know, for me, there are certain things that I enjoy

doing from from years ago, you know, I still, I still write

code in using VI. And, you know, I still spend a lot of time on

the command line on Linux systems. But if I never have to

write another regex, again, I'd be a happy person. So, so that

so the power of things like natural language processing

just, it impresses me, and also improves my daily life. So there

you go. That's, I answered that question.

Jason Baum: Great. Awesome. I love it. You should have been

interested in interviewing yourself. And you would also

have gotten through that line better than I just did. Well, I

really appreciate your time, Richard and educating us on AI

ops, ml ops, and you know, how it fits into into DevOps as a

tool and just in general makes our lives easier and not coming

to cause doomsday. So I really appreciate you coming on.

Richard Whitehead: It's all good. It's not Skynet.

Jason Baum: Thank goodness. If anyone names our company Skynet,

I think question there. There. Well, maybe just funny, I don't

know. Well, thank you so much, Richard, I really appreciate

your time. And thank you for listening to this episode of the

humans of DevOps Podcast. I'm going to end this episode The

way I always do, encouraging you to become a member of DevOps

Institute to get access to even more great resources just like

this one. Until next time, stay safe, stay healthy, and most of

all, state humans live long and prosper.

Narrator: Thanks for listening to this episode of the humans of

DevOps podcast. Don't forget to join our global community to get

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