Bob Friday, Chief AI Officer, Juniper Networks, and Mist Systems Co-Founder

Real AI: A Reality Check Beyond the Hype

AI in Action 23Bob Friday
Bob Friday

In this video, watch as Juniper Networks’ Chief AI Officer, Bob Friday cuts through the buzzwords and misleading narratives the ‘other guys’ are touting, and offers a comprehensive and eye-opening exploration of what makes proven artificial intelligence... real.

1:15 Defining AI for IT and AIOps

3:50 Creating AI from the Ground Up

6:05 The Importance of Good Data with Good AI

9:13 Zoom & Microsoft Teams Integrated with Marvis

10:59 The Data Science Toolbox

13:19 Self-Driving Network Outlook

15:10 What is a Virtual Network Assistant (VNA)?

17:40 What’s Next for AI & VNAs?

20:06 Leveraging AIOps and Real AI

22:40 Integration between AI Systems

24:18 Advice on Building AI

25:34 Closing Statements

Show more

You’ll learn

  • What to expect from AI in Action on-demand

Who is this for?

Business Leaders Network Professionals Security Professionals


Bob Friday
Bob Friday
Chief AI Officer, Juniper Networks, and Mist Systems Co-Founder

Guest speakers

Jeff Aaron
Jeff Aaron
VP of Enterprise Marketing, Juniper Networks


0:03 [Music]

0:12 hello everybody Welcome to real AI a

0:15 reality check beyond the hype I'm Jeff

0:17 Aaron VP of Enterprise marketing at

0:19 Juniper and we have Bob Friday GPI

0:21 officer good to be here Jeff yeah

0:23 awesome this is actually a little bit

0:25 strange because Bob and I have had so

0:26 many conversations throughout the years

0:28 but usually there's a bottle of wine in

0:30 the middle for those that don't know Bob

0:31 is a Vintner and actually a pretty good

0:33 one I must say actually I'm not I'm not

0:35 just saying that yeah one one Barrel

0:37 wine a year and yeah we wine and air

0:39 both topics near to my heart so we turn

0:41 this into the wine show and we do work

0:43 that analogy into a lot of our

0:44 conversation which is good but obviously

0:46 real AI is what we're going to talk

0:47 about here and uh you know what I think

0:49 we really want to just peel back the

0:50 curtain a little bit uh you know there's

0:52 a lot of AI washing out there so what

0:54 goes into an AI system how does it

0:56 differentiate how do you build one and

0:58 obviously you're the guy who did it uh

1:01 in fact we we go back and that we worked

1:03 at airspace which was a Wireless company

1:04 bought by Cisco and then we worked at

1:07 Mist which I think some people thought

1:09 was a Wireless company and it was but it

1:11 also was an AI company right we were the

1:13 first to kind of do AI for it so I guess

1:15 maybe we start off how would you define

1:17 AI for it and AI Ops and and why in 2014

1:21 When Miss was founded was that the time

1:23 to really bring this to Market yeah you

1:25 know people ask me that and you know for

1:27 me I look at Ai and AI Ops you know it

1:30 really for me is the next step in the

1:32 evolution of automation right you know

1:34 and if you look what AI really stands

1:36 for it usually means doing something on

1:37 part of the human right and I see that's

1:40 what happening with networking right

1:41 right we're now trying to build a

1:43 solution that can actually do something

1:45 on par with a it expert yeah you know

1:48 and we were just talking about Watson

1:49 right and that was one of The

1:50 Inspirations for missing Jeopardy right

1:52 Watson Jeopardy 2011. that was like you

1:55 know if they can build something that

1:56 can play Jeopardy yeah you know we

1:58 should be able to build something they

1:59 can play networking Jeopardy so what

2:01 happened 24 like why was that the time

2:03 where all of a sudden you you said this

2:05 is something we can build into a system

2:06 you know there's a couple things that

2:08 happened in back in the 2014 time frame

2:10 I mean one was you know I was working at

2:12 a big company you know and we were

2:14 listening to customers back in the early

2:16 days of Wi-Fi when Wi-Fi went from nice

2:18 to have to a must-have but then it went

2:19 to business critical you know and we

2:21 started to hear from big companies that

2:23 before they were going to put anything

2:24 on their Network they wanted to make

2:26 sure that you know controllers stopped

2:28 crashing they wanted to you know keep up

2:30 with their digital transformation

2:31 project you know so they wanted code

2:33 released every yeah every weeks and not

2:36 years and probably most importantly they

2:39 started to want to make sure that when

2:40 they put a critical app on that thing

2:42 they want to make sure the app or the

2:43 user experience was really going to work

2:44 yeah it was a kind of a shift from

2:46 focusing on the network experience

2:47 passing traffic uptime to really

2:49 focusing on the user experience yeah you

2:51 know so we still have to keep everything

2:52 green right APS router switches all that

2:55 but beyond that it was really more

2:57 important to keep the applying to Cloud

2:59 experience up and running and I would

3:00 say the other thing if you look what

3:01 interesting happened in 2014 you know if

3:03 you look at the Google search statistics

3:05 2014 was the year that

3:08 AI really went from kind of a research

3:10 topic you know to a reality yeah it

3:13 became real and I think there was a

3:14 perfect storm of things that happened

3:16 back then yeah it was like Cloud gpus

3:18 you know we got access to low-cost

3:21 compute storage Amazon Google all that

3:24 stuff became real yeah models became

3:26 bigger you know tensorflow the tools to

3:29 build all the stuff became widely

3:30 available so if you look back 2014 was a

3:33 year a lot of AI startups you know if

3:35 you look at myths you know interestingly

3:38 we didn't really start as an AI you know

3:40 if you talk to sujay it was really more

3:42 of a vision of day two operations and

3:44 making sure we could build an

3:46 architecture Cloud that could handle a

3:48 lot of telemetry data yeah so I mean you

3:51 were and sujay were at the biggest

3:53 networking company in the planet at the

3:55 time and obviously you realized or you

3:57 thought that you couldn't get it done

3:59 there it's kind of a little bit of the

4:00 inventor's dilemma so walk through that

4:04 I mean why did you need a clean sheet of

4:05 paper to do real AI you know why

4:07 couldn't you get there from the old

4:08 architectures well I think you know back

4:10 in that time frame we had just you know

4:11 at Cisco we just acquired Meraki big

4:14 cloud company

4:15 um and you kind of look like what we're

4:17 trying to build the vision was that like

4:19 I said to build something that could

4:20 process a lot of data in real time for

4:23 day two operations

4:25 you know and fundamentally when we

4:26 looked at it it missed is actually a big

4:28 architectural bet

4:30 right we were basically betting that hey

4:31 we really needed a blanket of paper we

4:33 had to change the underlying Foundation

4:36 and I think what people don't fully

4:37 appreciate is moving things to the cloud

4:39 is not as simple as taking controls and

4:42 throwing analyzing and sticking a Docker

4:43 there's really a major software

4:45 architectural change yeah you know so

4:47 when 20 years ago when I was doing

4:49 Aerospace and putting code on Linux

4:51 boxes and shipping software on Linux

4:52 boxes when you move to This Cloud

4:54 architecture you're really moving to

4:56 microservices yeah much more redundant

5:00 much more reliable it actually was

5:01 really interesting to me to again to

5:03 build a real AI engine you guys actually

5:05 looked at like Netflix and Twitter and

5:08 Linkedin how they built a cloud I mean

5:09 that was more the inspiration for for

5:11 the Miss Cloud than than you know

5:12 additional networking I mean outside of

5:14 networking right you know if you look at

5:16 some of the big Netflix you know people

5:18 were already moving to microservices

5:20 architectures continuous integration

5:22 tests yeah you know these are

5:24 architectures where you basically were

5:26 uploading code every day every week with

5:29 reliability yeah and that was part of

5:31 that original customer you know if

5:33 you're going to do digital

5:34 transformation their mobile app that

5:37 part of the thing was basically moving

5:39 very fast yeah and their infrastructure

5:41 was still moving at the speed of

5:43 years months you know they want to be

5:45 moving up weeks yeah so that was kind of

5:47 the inspiration of yes we can basically

5:49 take a new Cloud architecture into mist

5:52 and to be honest that's really hard to

5:55 do in big companies you know if you're a

5:57 big company trying to change the

5:58 foundation of something is something

6:00 better done with a blank sheet of paper

6:02 inside of a big company yeah I can

6:04 imagine that um you mentioned data right

6:06 and I think going back to your wine

6:08 analogy right you know you can only make

6:10 good wine with with good grapes Bob's

6:12 mentioned that a thousand times and it's

6:14 applicable same thing with AI right you

6:16 can only make good AI with with good

6:18 data and I think that is one of the

6:20 things that is part of that architecture

6:22 shift right

6:23 um you know again when I walked in the

6:24 door and missed you know I heard the

6:27 notion of you know rewriting them the

6:28 control plane and collecting 150 user

6:30 States every two seconds and my mind was

6:32 kind of blowing on that and so talk us

6:34 through I mean

6:35 what kind of data in is it a quality

6:38 issue or is it a quantity issue right

6:39 when you talk when you hear some of

6:41 these other vendors out there just think

6:42 oh we have a thousand bazillion more APS

6:44 than anyone else so we're better at Ai

6:46 and I feel like that that story doesn't

6:48 really hold water so well I mean I think

6:50 you know when we started myth right I

6:51 think Suzanne we got a lot of grief on

6:53 you okay why does the industry need

6:54 another access point you know and it

6:56 wasn't because we thought we needed

6:57 another access point in the industry is

6:59 because we wanted to get the right

7:01 technology right yeah you know if you

7:02 look at what we did 20 years ago at

7:04 airspace you know we were sending data

7:05 back every minute to controllers and

7:08 doing symmetrically you know asympt

7:10 symmetric and everything you know when

7:12 we're doing this user experience thing

7:14 you know now we're sending data back at

7:16 every user State change you know when

7:18 you connect authenticate yeah and so

7:20 that was probably the other thing if

7:22 that's happening in the industry is

7:23 we're going from a networking SNP world

7:26 where you're pulling data out you know

7:28 to where these network devices are

7:30 really becoming sensors and sources of

7:32 telemetry yeah so that was the reason

7:34 why we decided you know you have to

7:36 build a cloud-friendly networking

7:39 element if you really want to get the

7:40 data you want to solve user experience

7:43 type of problems and it's jumping

7:44 forward uh you know five years-ish but

7:46 that's also why Juniper Miss came

7:48 together right you know now being able

7:50 to pull Telemetry from routers which is

7:52 security devices firewalls you know

7:54 Wireless you know I would imagine that

7:56 completes the puzzle quite a bit yeah I

7:58 mean if you look when we started we've

7:59 really focused on the access point in

8:01 the edge because we were trying to

8:02 answer the question of you know if

8:04 you're having a poor internet experience

8:06 it turns out that the edge the access

8:08 point has about 80 percent of the data

8:10 you need to answer that question yeah

8:12 you know since we've joined Juniper now

8:14 you know we've started extend that AI

8:16 Ops Marvis framework across the wireless

8:18 the switch the route you know and when

8:20 you get to the router that starts to

8:22 bring in the application layer yeah and

8:24 so that starts to let us start answering

8:25 more questions like you know why is your

8:27 team zoom call having problems yeah and

8:30 so the access point in the edge gives

8:31 you a lot of Layer Two connectivity

8:33 Telemetry the router gives you a lot of

8:35 layers three application Telemetry yeah

8:37 so I mean I actually find that

8:39 fascinating right um you know the notion

8:41 of can you do Ai and silos right you

8:43 know can you have an sd-wan solution

8:44 here a wired Wireless solution here or

8:46 security solution here if they're not

8:48 all together I mean are you really going

8:49 to be able to do end-to-end event

8:51 correlation or we call client to cloud

8:52 and that's obviously one things that's

8:54 impressed me about what we've been able

8:56 or you've been able to deliver at

8:57 Universe I think that's that's pretty

8:58 interesting yeah and I think that's a

9:00 vision and you look at Juniper right

9:01 we've extended that

9:03 Telemetry all the way from the client to

9:06 the wireless AP to switch the router you

9:08 know we're starting to extend that

9:09 Telemetry all the way into the cloud

9:11 application yeah so there's a recent

9:13 announcement with zoom for example yeah

9:15 walk through that because I thought that

9:16 was that was really interesting because

9:17 it's funny we've always been saying for

9:19 years right you know with the client to

9:22 Cloud you can troubleshoot you know

9:24 what's wrong with Bob's Zoom call but we

9:25 were missing kind of a key element right

9:27 we're focusing on the network and not

9:28 necessarily the application side and so

9:29 I think that's an interesting shift

9:31 right yeah I mean so that was really the

9:32 you know you know when you're in the AI

9:35 data Science World labeled data is like

9:37 gold okay you know if you talk to any

9:39 data science who's trying to build a

9:40 model you know if you get label data you

9:43 can train that model you know so what we

9:44 announced that Mobility field day

9:46 recently was basically starting taking

9:47 data from the application layer and the

9:50 application layer knows when something's

9:52 gone wrong they may not know what went

9:53 wrong but they know something so that's

9:55 labeled so now we have label data from

9:58 your collaboration Zoom teams call and

10:00 we're conjoining that with your network

10:02 data yeah right once you've done that

10:04 now we can build models that can

10:05 accurately predict your Zoom performance

10:08 or your team's performance you know and

10:10 once you've actually done that now you

10:12 can interpret that model yeah you know

10:14 once again actually predict your

10:15 performance then that's the power of AI

10:17 right and that's when you're that's

10:19 really AI I mean that goes beyond just

10:20 normal machine learning to really be

10:22 able to you know do anomaly detection

10:23 Predictive Analytics self-driving I mean

10:26 that's that's all that's all becoming

10:28 real so that's that's awesome yeah I

10:30 mean if you look at it you know if you

10:31 look in the history of machine learning

10:33 right there's tons of AIML algorithms

10:36 that have been around for decades you

10:38 know what's really transforming the

10:39 industry in networking outside network

10:42 is really these deep learning models

10:44 right and that's what we saw with chat

10:46 yeah last year right it's like these

10:48 models are getting much more complex and

10:50 bigger and it's really tons of data that

10:52 you're using training yeah and those are

10:54 the disruptive

10:55 models I want to talk about that more in

10:58 a second before we get there you

10:59 mentioned data science right and you

11:02 mentioned MFD um you know I was at an

11:04 MFD once where you know I said we were

11:06 the first with AI Ops and you know data

11:08 science and someone's like does the

11:09 first really matter does being first

11:11 really matter and I kind of felt when it

11:14 came to AI yeah it does right I mean

11:17 you're learning your data science

11:18 algorithms are getting better

11:20 um you know I remember when Alexa first

11:21 first launched right I said who's the

11:23 quarterback of the 49ers and the answer

11:24 was Joe Montana is a famous quarterback

11:26 of the 49ers it's like no that's not

11:28 what I asked and now if you ask it to

11:29 tell you who played last week and what

11:31 their stats were so talk about you know

11:33 the data science toolbox you know what

11:34 kind of you know went into mist and

11:36 again you know why it matters for real

11:39 Ai and how it differentiates yeah I mean

11:41 I think in the industry right now we're

11:43 all using the same underlying algorithms

11:45 you know but the really big thing around

11:47 the data science is really the team you

11:49 know if you look what happened with

11:50 openai right it didn't pop you know last

11:53 year they didn't magically pop out of

11:55 the words that was five years years of

11:56 work that went into going from TPT one

11:58 two three four you know and if you look

12:00 what you don't realize there's gpt3 that

12:02 really broke out of the gate right it's

12:03 been around for a while yeah no you look

12:05 at Mist right you know when we started

12:07 this adventure I had you know took a

12:08 year to get the cloud built you know and

12:10 then it took time to actually get the

12:12 data you know the first mission I missed

12:15 was really basically getting your

12:16 support team to stop sshing into devices

12:18 yeah right because if you're going to do

12:20 AI Ops you got to get that data to the

12:22 cloud yeah right and that by itself you

12:24 know figuring out what data to get to

12:25 the Cloud is a journey yeah and I mean

12:28 I've heard examples where as the data

12:30 science algorithms get better you do

12:32 things like less false positives you can

12:34 do things like more feature sets like

12:36 service levels or finding failing

12:39 clients and things like that so

12:40 obviously it gets more more robust and

12:42 better with time right yeah I mean if

12:45 you look at an army detection I mean

12:46 that's kind of a classic networking

12:48 problem that's been around for years

12:49 yeah uh you know we've been trying to do

12:52 it with arima and other statistical

12:54 approaches and the false positive

12:56 basically generated more noises you know

12:58 no network admin wants to be woken up at

13:00 three in the morning for a crying wolf

13:01 right yeah right you know but now we've

13:04 gone you want to call them only when

13:05 there's a problem you want to get down

13:06 that false you know positive it's got to

13:08 be almost zero yeah and that's basically

13:10 where things like lstm up in that deep

13:12 learning category yeah are starting to

13:14 really transform Network right yeah how

13:16 we can actually build an army detections

13:17 that don't cry wolf yeah and I know

13:19 there's like this Holy Grail out there

13:20 self-driving right you know kind of like

13:22 autonomous vehicles has their stages of

13:25 autonomy and you know sort of us there's

13:27 like a stage of of self-driving where

13:29 you know the network fixes itself right

13:31 do you think we'll ever get there I

13:33 think we're on the way there I mean if

13:35 you look what we're doing right now

13:36 there's all types of cases real Resource

13:38 Management it's kind of the classic one

13:39 that's been it's kind of self-driving of

13:41 adjusting the power control channels in

13:44 your network that's already happening

13:45 which by the way great story there we're

13:47 at a customer Retreat and I'm not going

13:49 to mention the customer but uh you know

13:51 there's a guy who said I will never let

13:53 you know the system automate my RRM and

13:56 we did that's where we basically you

13:57 know ran through things and he chose

13:59 what channels and and outputs and and

14:01 our system did and he was kind of Blown

14:04 Away he's like this is the first time

14:05 ever it actually mirrored what I would

14:07 do so yeah I remember that right yeah I

14:09 mean I think you know six gigahertz is

14:11 probably example guys there's too many

14:13 knobs for the average person to try to

14:14 get things adjusted right you know so

14:16 that's an example where that's

14:17 self-driving I think we're starting to

14:19 see things like vlans missing vlans

14:22 these are things that the AI can detect

14:25 much easier than the human and those

14:27 type of things are going to be starting

14:28 to get self-correcting now and you would

14:30 argue this also then ties back to the

14:31 need for you know a full stack right if

14:34 you don't have the ability to go change

14:36 something on the land or change

14:37 something on the wired Wireless you're

14:38 only going to be able to just give a

14:39 recommendation at best right and so you

14:42 know that self-driving needs that full

14:44 staff to come together yeah in the data

14:46 Science World what we call feature

14:47 engineering right and so when you're

14:49 trying to build these models you know

14:51 predicting your Zoom team's performance

14:53 you know you want features across the

14:55 whole stack right I want features from

14:56 the client the access point and the

14:59 router and all the way to the into the

15:01 data center right because ultimately

15:02 when you're tracking down problems you

15:04 want to get down to what networking

15:06 feature is causing your poor experience

15:08 yeah yeah so the step right before

15:12 self-driving is a VNA a virtual Network

15:14 assistant right like uh in the Juniper

15:16 world we have Marvis

15:19 um walk me through that a little bit I

15:20 know Marcus is your baby uh you know I

15:22 think you even probably named it Marvis

15:23 back in the day

15:27 Jarvis and all that and are we going to

15:30 be sued but that's fine

15:31 um but you know talk you through it like

15:32 what what in your mind is is a VNA and

15:35 what does it bring to the table yeah you

15:37 know I think this is really around this

15:38 conversational interface you know when

15:40 you interact with your network you know

15:42 you're either in a troubleshooting mode

15:43 and trying to figure something out over

15:45 in the self-driving mode yeah you know

15:47 so if you look at what we've done

15:49 marvelous and Ops you know we have kind

15:53 of the action framework for self-driving

15:55 but we also have a conversational

15:56 interface for troubleshooting yeah you

15:59 know and I think you know what we've

16:00 seen with large language models we

16:02 started this interface probably four or

16:04 five years ago did really good at

16:06 natural language understanding you know

16:08 but what llms are bringing is natural

16:11 generation now yeah right and I think

16:13 this is the vision of uh you know my

16:15 Star Trek analogy you know if you look

16:17 at Star Trek almost all those

16:19 technologies have become real I think

16:21 you know talking to your networking

16:22 computer is the next star track analogy

16:25 yeah technology yeah that is going to

16:27 bite the dust yeah I think we're gonna

16:28 be down to teleporters the last piece of

16:31 Star Trek that needs to be brought you

16:33 out there

16:34 um

16:35 I do want to double click on that kind

16:37 of the llm bringing in the journey

16:38 because I think you hit on something

16:39 interesting you know our perspective is

16:42 it's just it's it's another

16:43 conversational interface it's another

16:45 add-on so where do you see it providing

16:48 you know a lot of value to what we're

16:50 doing where do you see it kind of being

16:52 tangential like how do you see it kind

16:53 of living together I think you know in

16:54 the troubleshy I think it's going to

16:56 make it a lot easier for it Network

16:59 admins in the future to actually get

17:01 information from a you know a much

17:04 growing complex network no I think you

17:07 know we have to look at most Network I.T

17:08 admins they start their careers with

17:10 clis we've slowly moved them from clis

17:13 to dashboards to help and make things

17:15 easier to manage I think this next

17:17 transition is going to be moving from

17:18 these dashboards into these

17:20 conversational interfaces

17:22 um I think open AI I mean we are another

17:25 step closer to you know talking to Kim

17:28 you should be talking to your network

17:29 and asking you know what's wrong today

17:31 you know why do you have a stomach ache

17:32 so AI killed the UI star you can quote

17:35 me on that that's our next webinar so

17:37 we're gonna use that one

17:38 um

17:39 um so what do you see coming next right

17:42 I mean for starters I think llm took a

17:44 lot of folks by surprise it created a

17:45 lot of Groundswell around this industry

17:47 a lot of you know folks figuring out how

17:50 to how to do that and

17:51 um you know even then I have some

17:52 concerns of you know our competitors

17:54 will launch an Ln Solutions say this is

17:56 a VNA and and obviously it's very

17:57 different but I'm curious on your take

18:00 on that I mean I think it's clear that

18:02 with llns right we're going to see

18:03 conversational interfaces become real

18:05 yeah you know we're going to start to

18:07 see them helping actually to think I

18:09 think you know next thing is really

18:10 around troubleshooting you know and how

18:12 we really solve real-time

18:13 troubleshooting loms are not going to

18:15 solve that real-time troubleshooting

18:17 problem it's a time stamp um they're

18:18 definitely a model at a point in history

18:19 right so it's good for knowledge base

18:21 but not for real-time questions I mean

18:23 so we'll definitely see llms basically

18:24 help with the knowledge-based stuff you

18:26 know basic questions you'll see my LED

18:30 blinking or you know what is EVP and

18:32 vxlan right questions like that and I

18:34 think we'll even see llm start to help

18:36 the uh network data in databases you

18:39 know where we start to build text to SQL

18:42 translators oh interesting no making it

18:44 much easier to get data out of the

18:46 database so I think that we're going to

18:47 see happening uh the real-time

18:49 troubleshooting is still going to

18:51 require domain expertise and data

18:54 scientists nice so to bring it all

18:56 together you're still obviously need the

18:58 right software architecture need the

18:59 right data need the right data science

19:01 you know you can't just overnight say

19:03 hey we're going to work with open Ai and

19:05 and and and deliver everything you need

19:07 in a networking solution no I mean I

19:09 think you know the journey to building

19:10 AI Ops you know starts with like I said

19:12 this Cloud Foundation but I think the

19:15 other piece people don't fully

19:16 appreciate you know there's the

19:17 technology piece and there's also this

19:19 organizational piece yeah right and I

19:22 think that's one of the things I found

19:23 you know another reason why I we left

19:26 Cisco to start missed was Oregon State

19:28 it's hard to do that in a big company

19:30 it's hard to change organizations inside

19:32 a big companies and I think where you're

19:34 referring to that is like having the

19:35 data science team sit with you know the

19:37 the support team you know uh and the

19:39 engineering team right so they're all

19:41 kind of looking at the problems coming

19:42 in feeding it back into the system

19:43 getting better I think that's that's

19:45 what we're alluding to in terms of

19:45 organization yeah I mean I think I

19:47 always highlight the people it's like

19:48 once we move to these Cloud Solutions

19:50 your support team has really becomes a

19:52 proxy for your customer right I mean

19:54 once I get the data to the cloud your

19:57 support team is the ultimate customer

19:58 you know if you make your customer

20:00 support team happy you know the fewer

20:02 tickets they see that means the fewer

20:04 tickets your customers are generating

20:05 and and we've I think every event we've

20:07 been at we've shown that Marvis efficacy

20:09 slide why don't you describe what that

20:12 is what are what are our goal is on that

20:13 for those that probably haven't seen it

20:15 yeah I mean I think that's the other

20:16 part of the journey it's like hey you

20:17 know if you're going to go down the

20:18 eiops you've got to eat your own dog

20:20 food you know so since we make your own

20:22 champagne Bob drink your own wine

20:25 so anyway I think that is the other big

20:27 aspect of this is hey if you get your

20:29 support team to actually use your AI Ops

20:31 you know and that's where I said it took

20:34 a year to figure out why I had my

20:36 support team SSA 18 into these devices

20:38 right because if I'm doing Cloud AI I've

20:41 got to get that data back to the cloud

20:42 and and so we would collect charts on

20:44 you know is the answer in the AP is the

20:46 answer in the VNA is the answer in the

20:48 cloud do we not have the answer at all

20:50 yeah we'll walk through that because I

20:52 think that's really a very foundational

20:53 element on how you build real AI right

20:55 to get to the root of can you solve the

20:57 problem yeah so that was an example

20:58 where the poor team actually used Marvis

21:00 to try to answer every support ticket

21:02 coming in and the data science team

21:04 reviewed those tickets with them every

21:06 week to figure out if Marvis didn't get

21:08 the answer why didn't we get the answer

21:10 is it because the data is still an AP

21:11 and so I was basically making sure we

21:13 can get the data from the EP that was a

21:15 year-long you know and that's part of

21:16 the it takes time to actually build

21:18 these AI Solutions because you have to

21:20 get the data you know and then you have

21:22 to figure out what features you actually

21:23 need and what I found interesting is you

21:25 know during that time you know the

21:26 number of tickets didn't necessarily go

21:27 up um so things were obviously getting

21:29 better but

21:31 um it yeah yeah it's just um there's

21:34 just it's very interesting in terms of

21:35 the data you can pull from that and and

21:37 like you said it just takes time it

21:39 takes time to build that out and what's

21:41 what I find interesting is that now it's

21:43 like the easy problems have been

21:45 answered so now it's the hard problems

21:46 that you're kind of focusing the

21:47 problems that are worth waking someone

21:48 up for right well I think that's part of

21:51 the journey is you start to solve the

21:52 low-hanging fruit problems right you can

21:54 see the graph go up quicker but as you

21:55 start to get towards parallels because

21:57 that last 10 gets harder and harder and

21:59 I think that is where if you look in the

22:01 data science

22:02 it's hard to hire data scientists who

22:04 are really networking experts yeah and

22:06 so that's why you've got to get you know

22:07 get your data science team but your

22:09 support team is the domain expert your

22:10 support team knows how these networks

22:12 work yeah and that's why you got to get

22:14 the right that's marriage between

22:15 because I know I've seen you in accounts

22:17 where you said you want to know if

22:18 someone's using real AI ask them how

22:21 their support team interfaces with their

22:22 data science yeah I my take right now is

22:25 you know Miss is the only ones actually

22:27 using their own AI Ops in their support

22:29 team yeah you know if you go to a vendor

22:32 and ask him it's like

22:33 what is your support team using the

22:35 answer support tickets if they're not

22:36 using their own AI Ops solution they're

22:38 not on the journey yet yeah

22:40 um one other thing I want to talk to you

22:42 a little bit um kind of what's next that

22:44 we're starting to see here even at AI in

22:45 action is

22:47 um integration like between AI systems

22:49 like you know servicenow for examples

22:51 here we've done a lot of integration

22:52 with them you know where do you see that

22:53 going and what what excites you about

22:55 that yeah I know this is an interesting

22:57 one because I think what we're seeing in

22:58 the networking World

23:00 um in the past we all used to send

23:01 networking events up to the systems

23:03 above us you know solarwinds some other

23:05 it's Splunk or something we're starting

23:07 to see networking move to AI events

23:11 right we're seeing a bunch of

23:12 distributed AI systems is that

23:13 standardized at all or I think we're at

23:15 the very beginning of telemetry I mean

23:17 we've seen standards go on to try to how

23:19 to configure network devices Telemetry

23:21 standards are just starting of you know

23:23 how do we make sure we start

23:24 standardizing what data is going to be

23:26 sent back from these networking elements

23:28 but what we're seeing what happened in

23:30 the AI stuff is we all start to filter

23:32 these raw Network events through AI

23:34 events we're seeing a much more

23:36 intelligent events above us and I think

23:38 that's what the industry is dealing with

23:39 is you know how do you deal with a bunch

23:40 of distributed AI events AI systems

23:43 across across the network yeah and I

23:45 know for for folks that are in Vegas

23:47 servicenow is going to talk about it but

23:48 for folks that are live on stream why

23:50 don't you just give a quick snippet on

23:51 what we're doing with servicenow because

23:52 I think that's really interesting

23:54 I mean if you look at servicenow they're

23:57 becoming kind of that consumer of all

23:59 these AI events yeah right and so

24:01 they're becoming kind of the next I

24:03 always call a generation of

24:05 AI above us right you know as we send AI

24:08 events they're taking AI events across

24:10 different systems and now it's starting

24:11 to correlate those events together to

24:13 help solve even bigger problems across

24:15 the network got it pretty cool

24:18 closing out a little bit any advice

24:22 um you know any advice for someone going

24:23 down this journey starting out saying

24:25 you know I want to build real Ai and not

24:28 go down a path a dead-end path you know

24:30 what would you give them well I mean I

24:32 think at a high level I always you know

24:33 people are starting the journey whether

24:35 startup or new adventures

24:38 this is the ultimate team sport you know

24:40 if you're going to start a you know a

24:41 New Journey whether it's a startup or an

24:43 adventure you got to realize that it

24:45 takes a team yeah you know and it takes

24:47 a team of data scientists support people

24:50 and organization sales marketing I don't

24:53 think people fully appreciate it

24:55 or or even we're not going to go down

24:57 here because that's a whole other

24:58 session of

24:59 legal ethics uh you know obviously AI

25:03 touches on a lot of points now that I

25:04 think are pretty interesting right yeah

25:06 I mean I think you know when you go down

25:07 that path you know there will be

25:09 compliance you know if you're headed

25:10 down the AI path you have to be aware of

25:12 the new compliance laws

25:14 um ethics you have to be aware of you

25:16 know how you're using AI that means

25:18 those are all aspects of AI that you

25:20 have to actually take into account when

25:21 you start to start your journey very

25:23 cool so I would imagine at some point we

25:25 won't be having this conversation it's

25:26 just be the the chat is doing it I'm not

25:30 even sure we're having this conversation

25:31 yeah I'm sure we are it's a simulation

25:32 everybody

25:34 um so with that I want to thank you Bob

25:35 I enjoyed the conversation

25:37 um and thank you guys all for joining us

25:39 if there's anyone that wants more

25:40 information you can check out the

25:41 comments and also there's a chance to

25:43 win a free AP

25:45 um I always feel like test driving the

25:46 product is not a free bottle of wine not

25:48 everybody

25:51 usually when we're in a small setting

25:53 says is that if anyone's in Los Gatos

25:54 they can come to his house for wine I

25:56 don't know if you're prepared to do this

25:57 over here or not but give me yeah you're

25:59 in the Alaska let me know Mississippi

26:01 State wine tasting room is open the

26:03 Vista State wine tasting room I thought

26:04 it was Friday Estates the name changes

26:06 all the time now it's a day of the week

26:08 depends on the day of the week and how

26:09 drunk you are

26:10 um so again with that please check out

26:12 the link and thanks everyone for joining

26:13 us we really enjoyed it thank you Bob

26:15 thanks dude

26:19 [Applause]


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