Maribel Lopez, Industry Analyst, Author, and Technology Influencer

Bob Friday of Juniper Network on the AI w Maribel Lopez Podcast

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What is an AI-Native Networking Platform?

Bob Friday discusses how AI is changing the networking landscape and the big ways your company can benefit from the latest advances, like an AI-Native Networking Platform.

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You’ll learn

  • How recent changes in AI are impacting the networking industry

  • About AI for IT Operations (AIOps)

  • The benefits of an AI-Native Networking Platform

Who is this for?

Network Professionals


Maribel Lopez Headshot
Maribel Lopez
Industry Analyst, Author, and Technology Influencer

Guest speakers

Bob Friday Headshot
Bob Friday
GVP Chief AI Office


0:03 hello and welcome back to the podcast

0:06 I'm marabel Lopez and this is the AI

0:08 with Marbel Lopez podcast and today we

0:11 are joined here with Bob Friday who is

0:14 the chief AI officer and CTO of the

0:17 Enterprise for Juniper Networks hey Bob

0:19 how are you y doing great marille great

0:22 to be here and as I tell people I never

0:24 believe I end my career as a chiefa

0:26 officer but here we are you know Bob

0:29 I've seen you through a lot lot of

0:30 different iterations of the career and

0:31 it's always been something new and

0:33 exciting and it actually doesn't

0:34 surprise me at all that you're in AI

0:36 that you would have found that and that

0:37 it would have found you and that

0:39 probably leads us to our first question

0:42 and that's you know in in my opinion

0:44 you've been at the Forefront of working

0:46 with AI in the networking field for some

0:49 time uh and that used to be an AI always

0:52 spoke about was chips and everything was

0:53 about gpus and CPUs that maybe could act

0:56 like gpus but I thought maybe you could

0:58 spend a moment discussing in what you

1:00 believe has changed in the AI landscape

1:02 since you've been in it from a

1:04 networking standpoint maybe tell us for

1:07 example um what we couldn't do several

1:09 years ago that we can now do today or

1:11 how you see that whole thing

1:13 evolving yeah know you know for me

1:15 personally right you know I started my

1:16 career in the wireless space you know

1:18 and this whole Adventure really started

1:20 back at the during the Cisco Adventure

1:22 we were talking to some big customers

1:24 about putting some customer experiencing

1:27 on their Network and they basically told

1:29 sud he's like we're not putting anything

1:31 on this network until you can promise US

1:33 you know that things are not going to

1:35 crash you can give code to us faster and

1:37 more importantly you know you can

1:39 basically guarantee that the user is

1:40 going to have a great experience you

1:42 know and that's kind of where the change

1:44 started that paradigm shift from

1:46 managing Network elements to really

1:48 managing this in toin user experience

1:50 and that's where AI Ops actually got to

1:52 the thingss you know and what I've seen

1:54 has changed you know the 30 some years

1:56 I've been doing this um you know 30

1:58 years ago I did my Master's and actually

2:00 did it with the neural networks and you

2:03 know I you know so what I've seen

2:05 personally change as these neural

2:06 networks have gotten really big now that

2:09 they could do something interesting

2:10 right and and I think that's what we're

2:13 seeing with AIA you know it's really

2:14 this the next step in the evolution of

2:17 automation right and that is what is

2:19 changing and if you really look at the

2:20 Google data this interesting data point

2:22 you know when did this AI thing take off

2:25 if you look at Google it took off in

2:26 around

2:27 2014 that's when all the MLS searches

2:30 and if you look back in 2014 what really

2:32 came together was a combination of Open

2:36 Source right we saw all the libraries

2:38 come out that let us start to build

2:40 interesting AI things models got big

2:43 enough right that's kind of when all the

2:46 cloud got big enough where he had enough

2:48 compute and storage you know 30 years

2:51 ago I was shipping software on a Linux

2:53 Box

2:55 Bar yeah you know now I've got access to

2:58 AWS Google

3:00 you know there is no limit to Computing

3:02 the storage now it's just a a matter of

3:04 how much your how much your Amazon bill

3:05 is going to be at month so I would say

3:07 that is the big change I've seen over my

3:09 30 years is things have come together

3:12 now where you can actually build things

3:13 that we talked about 30 years ago thank

3:16 goodness and and I agree with you and

3:18 and cloud has been a big part of that so

3:21 what we see happening now is there's a

3:22 great deal of AI Marketing in the tech

3:24 space I mean it's everywhere basically

3:26 everyone is rolling out an AI enabled

3:28 product and it seems hard for Enterprise

3:32 buyers to understand and evaluate the

3:34 various Solutions because everybody says

3:36 they're doing AI so you've been in this

3:39 for a while and you have some

3:40 perspectives of what may be AI what may

3:42 not be AI but just from your perspective

3:45 what are some examples of functionality

3:47 or criteria that companies should be

3:49 looking for an AI

3:51 Solutions yeah so so this is another

3:53 funny story remember when I you know

3:55 when I talked to analysts like you they

3:56 were like hey Bob I we canot tell the

3:58 difference between your PowerPoint and

4:00 your competitor's PowerPoint from a

4:02 PowerPoint perspective it all looks the

4:04 same now I could tell you one of the

4:07 reasons and people ask me say you know

4:09 why did we decide you know why did suj

4:11 and I decide to build an access point

4:13 when we started Miss right it's not

4:16 because I thought the world needed

4:17 another wireless access point uh it's

4:21 because I really wanted to make sure I

4:22 could get the data I needed to answer

4:24 the question of why are you having a bad

4:28 internet experience

4:30 and so if you look at back to your other

4:32 question what's the difference between

4:33 now and you know 30 years ago 20 years

4:36 ago when I did

4:37 airspace one of the differen is the data

4:39 we're sending back right you know 20

4:41 years ago I was sending back data every

4:44 minute or two minutes back to a

4:46 controller synchronously you know one of

4:49 the Paradigm shifts here I missed now

4:50 I'm now sing data back on the user

4:53 asynchronous you know we're keeping

4:54 track of every user minute on the

4:56 network not the network element but the

4:58 user minute so I think that is one thing

5:01 when I talk to people about data making

5:03 sure you know does the vendor you have

5:05 the right data you need to answer the

5:07 question you're trying to answer I think

5:09 the other thing I've learned you know

5:11 and leaving Cisco to do miss was I knew

5:14 I had to build this real time Cloud

5:18 architecture you know that forced me to

5:20 you know okay I got to get a blank sheet

5:21 of paper uh but the other thing was

5:24 really about I had to organizationally

5:26 change making sure my data science team

5:28 could work with my customer support team

5:31 because when you move to This Cloud AI

5:34 model really the support team is a proxy

5:37 for your customers you know and what I

5:39 tell most customers of people you know

5:42 you should ask your vendor are they

5:43 actually using their own AI op solution

5:45 for their support team if they're not

5:48 they have not started the journey to

5:50 Cloud aops you know so the first step on

5:53 starting that journey is trying to make

5:55 your own support team happy because the

5:58 fewer support tickets that they see is

6:00 the fewer support tickets your customers

6:01 are sending you so that is the first

6:04 step on the journey to Cloud a

6:06 offs so let's just stop there for a

6:08 second because some people that are

6:11 listening to the podcast may have

6:12 varying levels of familiarity with the

6:15 term AI Ops could you define the term AI

6:18 Ops for the

6:19 audience yeah I mean so when I say AI

6:21 Ops usually what I talked about and I

6:24 agree with you people are confused

6:25 because really this is just the next

6:27 step in what they've been doing for 30

6:29 years right you know we've been

6:30 automating networks doing all a lot of

6:33 machine learning regression algorithms

6:35 we've been doing that for a long time

6:38 the fundamental difference that what

6:39 we're doing now is we're starting to

6:41 build solutions that have the cogon

6:44 reasoning skills of a human right we're

6:47 building solutions that can deploy and

6:50 operate networks on par with human it

6:52 domain experts and so the subtle

6:55 difference between the automation we're

6:57 doing now with deep learning versus

6:59 machine machine learning and when I say

7:01 deep learning these are actual models

7:03 that we train with tons of data this is

7:05 like that chat GPT thing right this is

7:09 you know where we're taking zoom and

7:10 teams data and building models that can

7:12 actually predict your user experience so

7:15 I think that is the subtle difference

7:16 when people try to understand the

7:17 difference

7:19 between ml machine learning and the

7:22 difference between these new deep

7:24 learning models and so when I say AI Ops

7:27 we're usually talking about some sort of

7:28 Deep where we're continuously learning

7:31 something about the network and these

7:33 tools that the IT department is going to

7:35 use they're almost like hiring a person

7:37 right you know you're bringing on this

7:39 virtual AI

7:41 assistant that doesn't do the same thing

7:43 every day in the old times we built

7:45 things that were very deterministic you

7:47 buil a model and it did it now we're

7:49 bringing on these AI assistants that

7:52 feel more like an a new intern you know

7:55 someone you have to learn to trust you

7:57 have to learn what they can and cannot

7:58 do and you got to trust they're going to

8:00 get better at what they're doing and

8:02 that's that continuous learning you know

8:04 that this AI assistant model is going to

8:06 learn your network and get better at

8:08 what it's doing well what I loved what

8:10 you just spoke about as well is also the

8:12 fact that it's the network connecting to

8:14 the app it's a full you know OSI stack

8:17 experience right so we're starting to

8:19 see that whole Loop come together which

8:22 I think the app was very disc connected

8:23 from that before so that's another good

8:25 piece of insight so one of the things

8:28 that we're seeing in the marketplace is

8:30 that companies are looking to have

8:32 strategic vendors to build platforms

8:34 that run the business so we have this uh

8:36 sort of pendulum where we go back and

8:38 forth between whether or not we want

8:39 lots of best breed whether or not we

8:41 want platforms it seems like we're in a

8:43 platform phase and I know that Juniper

8:47 Networks has specifically talked about

8:49 building something called an AI native

8:52 networking platform uh that's a lot of

8:55 words together AI native networking

8:56 platform but can you tell us what that

8:58 is and and what it means for

9:01 customers yeah you know the funny thing

9:03 is you know like when sui and I were at

9:05 Cisco you know we're trying to make this

9:06 decision right you know should we stay

9:08 you know do this at Cisco go off and

9:11 start a company and so when you say AI

9:14 native it's really like saying you would

9:16 need to start with a blank sheet of

9:17 paper right and what people don't fully

9:20 appreciate is you know if you're going

9:23 to build an AI native Foundation what we

9:26 realized is we really needed a blank

9:28 sheet of paper to build a new real-time

9:31 architecture right I knew I had to build

9:34 an architecture that could ingest data

9:36 real time and do something with that

9:38 data right that turns out and it also a

9:41 different software architecture right

9:44 you know when you go from kind of

9:46 building software on a Linux box to

9:49 building microservices in the cloud that

9:52 that by itself brings a ton of value to

9:54 customers right just getting data to the

9:56 cloud brings a lot more visibility and

9:58 observability

9:59 but it also brings a lot of

10:01 reliability right that's what's the

10:03 difference between a cloud environment

10:05 and a controller environment is that

10:07 software architecture those blast radius

10:09 get smarter so my location crashes it

10:12 doesn't affect my controller wireless

10:14 piece of the puzzle right so that is

10:17 kind of the thing I T think is different

10:19 when people say AI native um and I think

10:22 you actually look what's going on in the

10:24 marketplace right now I think it kind of

10:26 reflects where we are right now you know

10:29 how did Miss become a leader it was

10:31 basically that bet that there really was

10:34 an architectural change happening in the

10:35 industry right moving to Cloud AI Ops is

10:38 not something you're just going to pull

10:40 onto an existing framework you really

10:42 need to go down to that native

10:43 foundation and start from

10:48 scratch you know I agree that this is

10:50 something that's so important it's

10:52 almost like we're at the opportunity

10:54 with so many parts of this text stack to

10:56 just hit a refresh button and say okay

10:58 let's if we were going to do it from

11:00 today onward what would it look like and

11:02 I think that to me is what AI native

11:05 means just like Cloud native was sort of

11:07 the iteration before this you know now

11:08 we've got AI native so uh Insight

11:12 automation virtual assistance

11:14 conversational interfaces these are all

11:16 really hot topics in the AI space and

11:19 you have a product in the space called

11:21 Marvis um I love that name by the

11:24 way it's such a cool name uh can you

11:27 explain to the audience what Marvis is

11:29 is and how it's evolved since its

11:31 introduction yeah you know so Marvis is

11:34 basically a culation cumulation of all

11:38 the different attributes we've been

11:39 talking about here right you know so

11:42 conversational interface is one of those

11:44 attributes of next assistant and what I

11:46 tell people is if you look back over the

11:49 last 20 years we kind of moveed from CIS

11:53 to dashboards the next really next user

11:56 interface is really going to be these

11:58 natural langu anguage user interfaces to

12:01 really help people interact not just

12:03 with networking I think you're going to

12:04 see it across all the industry right

12:06 this is going to become the next big

12:08 thing and that's what chat gbt really

12:10 brought we always had kind of the

12:12 natural understanding piece what we

12:14 didn't have is a natural generation

12:16 piece right and that is what chat TPT is

12:19 really bringing that natural language

12:20 chat TPT that we actually can start

12:22 interacting you know with people and it

12:24 teams in a very more very much more

12:26 natural way I think the other thing

12:29 attribut to Marvis is this continuous

12:32 learning right and this is that piece of

12:34 you know hey we're moving from machine

12:35 learning and moving to this deep

12:38 learning automation where you're

12:40 continuously learning a network right

12:43 you're continuously building models

12:44 you're continuously adjusting Zoom data

12:47 teams data right so as the network

12:49 changes or something changes the models

12:51 are continuously adapting to what those

12:53 changes are you know so that's kind of

12:56 the Contin conversational inter phase

12:58 continu and finally there is the action

13:00 frame this action framework at the end

13:02 of the day you want to take all these

13:04 insights and turn it into some

13:05 recommendation or some action and I

13:07 think that's the industry change we're

13:09 seeing also is we're moving from that

13:11 SNMP Raw event world where we used to

13:14 send up thousands of events up to

13:17 something above us to where these

13:19 systems are now sending up more AI

13:21 relevant events right we're not setting

13:23 up raw events we're taking these raw

13:25 events from the network and actually

13:26 translating them into something that is

13:28 actionable so I think those are the

13:30 three main attributes of Marvis going

13:32 forward think conversational interface

13:34 think Marvis action framework and

13:37 continuous learning that is the new

13:39 piece that's disrupting everything okay

13:42 so in a way we can think of this is the

13:44 evolution of what we originally talked

13:47 about as self-driving

13:49 networks yeah I mean if you think about

13:51 self we're on that Journey right now and

13:54 the analysis I give to people you know

13:56 when it comes to Ai and uh I don't know

14:00 I suspect you're not a skeptic right I

14:01 would tell you the number of AI Skeptics

14:03 has gone down but I think what people

14:05 are seeing now you look at the healthc

14:07 care right you know I don't think I

14:10 think anybody who goes to their doctor

14:12 they're going to want to know that that

14:13 doctor is using the latest and greatest

14:16 AI for helping diagnose their disease

14:20 cancer right and we're seeing it with

14:22 cars right say we may not be a

14:24 self-driving cars but you're going to

14:25 want to know that that car has the

14:27 latest and greatest AI if you're going

14:29 to buy a car for your kid who's learning

14:31 Drive you're going to want to make sure

14:32 it has all the latest and greatest

14:34 safety so I think the same thing is

14:35 happened in a networking industry you

14:37 know when you connect to a network

14:39 you're going to want to know that it's

14:40 using the latest and greatest AI to make

14:42 sure that we have a great Zoom internet

14:45 experience makes total sense and like we

14:49 said earlier you know connecting it up

14:50 to the app and what you're trying to do

14:51 so we we've been talking about um

14:54 experience I know you and I talked about

14:56 experienced person atw working a while

14:58 ago and it seems like AI actually helps

15:01 us get one click deeper into that so so

15:04 we have Marvis now we've got Marva minis

15:06 I think that's actually quite cool too

15:08 so so so um when people hear marvous

15:12 minis you were just outling U outlining

15:15 the Marvis framework tell me where

15:16 marvus minis fits into that yeah so

15:19 Marvis we started I mean Marvis does a

15:21 great job when you have users on the

15:23 network trying to understand if they're

15:24 having a good experience or not right

15:27 you know and when I you know for those

15:29 who know me I make a barrel of wine

15:30 every year and it's like you know great

15:32 wine starts with great grapes great AI

15:34 starts with great data so the question

15:36 is what do you do when there's no one on

15:37 your network you know you need data

15:41 still have data to do any great AI so

15:43 Marvis minis is our answer to the

15:45 problem of day one networking how do we

15:48 make sure that network is going to work

15:50 before we turn it on how do we make sure

15:52 that network is ready to go at 8:00 in

15:54 the morning when all the users are going

15:55 to come on it so you think of minis as a

15:57 synthetic user user in the network that

16:00 Marvis can send around and gather up the

16:02 data needs to make sure that you're

16:04 going to have a great internet

16:05 connection on day one 8 o'clock whenever

16:08 you're ready to get on that Network so

16:10 that's Marva minis coming coming to the

16:13 market you know it's interesting because

16:15 um there's particularly as we think

16:17 about the AI landscape in general

16:20 there's been a lot of talk about digital

16:21 Twins and this is sort of the concept of

16:23 digital twinning for the network you

16:24 know trying to simulate something see

16:26 what happens I also like theide because

16:29 I think there's a great opportunity to

16:33 take synthetic data and test all

16:34 different kinds of things which is

16:36 basically exactly what you you said um

16:38 Marvis minis is going to do you help

16:40 people simulate or have a have a user

16:43 that simulates what the experience might

16:44 look like on a network before the

16:46 network goes live and you know we see

16:48 lots of opportunities in AI to have

16:51 either this type of experience or if you

16:53 don't have the data you need to actually

16:55 create synthetic data that you can run

16:56 with models to try to create the type of

16:58 model that you want to do so pretty

17:00 exciting work you guys are doing and I

17:02 think it's going to be really

17:03 interesting for uh people as they think

17:05 of Next Generation networks right

17:07 because it's it's a different animal

17:09 than what we've had in the past now we

17:12 say that the market is changing very

17:13 rapidly I know that you see the markets

17:15 changing very rapidly uh maybe you could

17:17 give us a bit of a look ahead you know

17:19 what are some of the things that Juniper

17:21 is looking forward to maybe it's topics

17:24 like AI Ops or other key topics you'd

17:27 like to address

17:29 yeah I mean back to the three attributes

17:30 of Marvis I think you're going see

17:32 conversational interfaces become that

17:35 standard interface going forward you

17:37 know I think it's a slow progression as

17:40 you know like it's hard to move people

17:42 off CLI we've been trying to get people

17:43 off CIS for 20 years so that's anline

17:46 interface will never die folks never die

17:49 but I think we're GNA see you know

17:51 natural language interfaces becoming

17:53 used for both public documents you know

17:56 trying to basically find out what's

17:57 going on you're going see become a u

18:00 even an alternative for business

18:02 intelligence right for exploring your

18:04 network data you know and you're going

18:06 to start to see that natur that

18:07 conversational interface become part of

18:09 your realtime troubleshooting experience

18:12 uh so you're going to see that evolve I

18:14 think what we're going to see evolve

18:15 even faster is this continuous learning

18:18 you know we saw what happened with chat

18:20 GPT when these models got big right you

18:23 kind of saw you know he we start to get

18:25 bigger and bigger models more data

18:26 training uh we we're going to start to

18:28 see that continuous learning part in

18:30 networking get more powerful just like

18:33 we saw with chat gbt and that all

18:35 results into more self-driving action

18:38 Frameworks networks that going forward

18:40 so that's where I see us heading

18:42 networking is that next step in the

18:44 evolution like I said before automation

18:46 right we're just gonna we're taking

18:47 automation to the next

18:50 level so I I love the way that you've

18:52 kind of Capstone some of the changes in

18:55 AI but if there's like one or two things

18:57 that you'd like to to close with it

18:58 you'd like to leave the audience

19:00 thinking about with AI what would it be

19:02 yeah you know when I try to summarize

19:04 more of us I usually try to remind them

19:05 you know like I said with the wine you

19:07 need to have the right data you know

19:09 that leads to the right response

19:12 recommendation and on top of that you

19:14 have to have an AI native infrastructure

19:16 secure

19:18 infrastructure makes perfect sense to me

19:20 we call those uh Righttime experiences

19:22 right information right person right

19:24 time on the right device so very similar

19:27 concept

19:28 now we usually like to leave the

19:30 audience with a new learning opportunity

19:32 and I know that you do a lot of learning

19:34 yourself as do I um is there a book a

19:36 podcast an activity that you'd like to

19:39 recommend to the audience could be Tech

19:41 related or not yeah it's you know so of

19:44 late I've been working on LM and uh I

19:46 met someone named alar antech right and

19:49 he had been working doing great work on

19:51 llm um and for networking type of things

19:54 and he's actually written a book called

19:56 machine learning for network and Cloud

19:58 engineers get ready for the era of

20:00 network automation so that's on my

20:02 latest reading list right now uh and it

20:04 kind of plays into that theme of next

20:06 generation of automation right awesome

20:11 well Bob thank you for your time and

20:12 attention and we always look forward to

20:14 hearing what you're building and what

20:15 you'll build next if anybody wants to

20:18 connect to you where can they reach out

20:21 yeah well you know AI op this is my

20:23 latest topic dear to my heart I would

20:25 just reach out to me on LinkedIn always

20:26 have to chop chat about AI Ops awesome

20:30 Bob Friday Juniper Networks Marbel Lopez

20:33 Lopez research thanks for listening

20:35 until next

20:38 time

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