Bob Friday Talks: Getting to know and love Marvis

0:00 thanks for joining us today we're going

0:02 to embark on a journey into the heart of

0:03 AI networking with our guest Shirley woo

0:06 we're going to dive into her insights

0:08 about Marvis the new iteration with

0:10 Marvis Minis and much more Shirley thank

0:12 you for joining us can you tell us a

0:14 little bit about yourself thanks Jessica

0:16 it's a great pleasure to be here so my

0:19 name is Shy I start the Mist for Juniper

0:23 for last five years right so I'm lead

0:27 Mist a Marv team and I'm senior director

0:31 so for the last five years we had a

0:33 great journey with Marvis and the AI of

0:35 course but now you know it's a great

0:38 question for me to look back for my

0:40 career and I actually just realized that

0:43 my career growth and the development

0:45 path is somehow totally aligned with the

0:48 industry growth so remember I remember

0:51 when I first finished my master's degree

0:54 in computer science I did spend quite a

0:56 few years to find my passion and the

1:00 Direction where I want to be what I

1:01 wanted to do even though I'm you know

1:03 have a daytime job in the software

1:05 development Cy industry so really

1:10 critical moment for me is that I decide

1:12 to jump into the startup so the first

1:15 ever startup company I ever joined was

1:17 in a data analytics uh space so we

1:20 trying to use the customer consumer data

1:23 to find the customer insights to predict

1:25 the customer turn and at that time that

1:28 you know that was before the Big Data AI

1:31 ml became a b word so we have a quite a

1:35 lot of challenges to deal with the data

1:37 Volant how we are going to apply very

1:39 accurate uh um statistical algorithms to

1:42 predict the you know customer turn so

1:46 then the other company had a very

1:48 successful acquisition so after the

1:50 acquisition I'm kind of reevaluated my

1:52 career where want to be what I want to

1:54 do so as that the same time that the Big

1:57 Data Hado and also cloud computing

2:01 became the best word that totally

2:03 revolutionize the software development

2:06 industry so that the moment I also St

2:09 into my second startup I joined the very

2:12 early um stage startup in cyber security

2:16 but even though the business problem is

2:18 different but the technology behind is

2:21 totally different different we utilize

2:24 cloud computing and Big Data tools we

2:27 trying to to identify abnormal user

2:30 behaviors to identify cyber security

2:33 threats so that was a really um uh you

2:37 know exciting Journey you know utilize

2:40 the AI and the big data to solve the

2:44 problem which is very traditional in the

2:46 cyber security space right so that

2:48 company also had a very you know uh

2:52 quite exciting Journey after acquisition

2:55 then you know time run into the Mist so

2:58 at the moment the same thing that hey

3:01 the Big Data actually came to another

3:03 chapter is a very mature space yeah so

3:06 people trying to utilize data cloud

3:09 computing AI trying to revolutionize the

3:11 traditional uh space like networking so

3:15 that's why came to the moment I joined

3:16 the mist and uh the mission is uh

3:20 utilize our Cloud infrastructure data

3:23 and AI how can we revolutionize the

3:26 networking operation so that's the

3:28 journey I came from so the you know the

3:31 I believe the really critical moment for

3:34 me is have a gut to find your passion

3:37 find whatever you believe and uh uh

3:41 trying to utilize data to solve the

3:43 problem so we know that Juniper's

3:45 customers love and use Marvis part of

3:48 juniper Juniper's AI native networking

3:50 platform can you give us an overview of

3:53 maybe the top two or three features of

3:55 the platform so Marvis actually is a is

3:59 a best word represent a lot of thing but

4:01 the core of the Marv I could think about

4:04 right as of today is that two or three

4:07 things first is Marvis is a you know not

4:10 more than one piece of software sits in

4:12 the cloud continue to S through the

4:16 stats and events streamed into the cloud

4:20 from juniper Cloud managed devices so I

4:24 will go through the live data stream to

4:26 we can close to near time near real time

4:29 be able to detect problems on the

4:32 customer's Network so this is the one

4:35 that we can have a we don't need the

4:37 network administrators continuous to

4:39 monitor their Network we can utilize our

4:42 data and the cloud to help the customer

4:44 to monitor the network corporations this

4:46 is a fundamental key difference compared

4:49 to traditional networking companies and

4:52 vendors so second important feature is

4:55 Marv not just detect the problems we

4:58 also can identify cause of the problem

5:01 if the problem is self fixable Marvis

5:04 will fix it but if the problem Marvis

5:07 cannot the fix they will gener create

5:10 the recommendations and send a web hook

5:12 email notification to the customer to

5:15 how to address this issue so this is the

5:18 fundamental two features but in the

5:20 recent news release you probably heard

5:22 about Marvis Mimi this is another great

5:25 feature for Mar that we release a

5:28 feature be able to auto made self

5:30 diagnostic analysis and be able to gener

5:33 report so that we can find the problems

5:36 before humans encounters them for our

5:39 customer so you just spoke about Marvis

5:41 minis which is one of the newer features

5:43 how have you seen Marvis evolve over

5:45 time when I first joined the m and the

5:47 Juniper early days that the Marvis just

5:50 be able to answer simple questions like

5:53 we call the virtual Network system so we

5:57 collect the stats events we build the

5:59 the time series and we can answer very

6:03 limited user queries that's early the

6:05 Maris so we when we join the Builder the

6:08 Marvis team we spend a significant chunk

6:11 of resources to to develop the Marvis

6:14 platform which be able to streaming

6:17 through um the live data to identify the

6:20 problem abnormal behaviors about the

6:23 network and also identify the um the

6:27 events and generate the root causes so

6:29 this Marvis event action framework we

6:32 developed on the cloud so this the

6:34 second phase of the Marvis now you know

6:36 the third is you know the we based on

6:39 the detected events actions we develop a

6:42 self um remediation framework and also

6:47 self optimization framework another one

6:50 is the latest Mar meaning we're talking

6:52 about the self dtic

6:54 framework so this is a mar on the road

6:57 map we have so far yeah

7:00 so you talked about like the different

7:01 layers there right and at the beginning

7:04 of it was the data which is where your

7:07 interest mostly lies um so what are the

7:10 foundational Technologies in terms of

7:13 the data piece of it that Marvis uses so

7:16 you brought another good question

7:18 typically people talk about Marvis is

7:20 hey is a data science team Marvis team

7:22 so I feel very embarrassed actually yes

7:25 we as a Marv team data scientists and

7:27 machine learning Engineers we contribute

7:29 a lot to maris's success but on top of

7:32 that actually we have a lot of Engineers

7:34 that are working on the cloud

7:36 infrastructure but most importantly our

7:38 firmware and Hardware teams they are the

7:42 the core to make sure our devices be

7:45 able to generate meaningful accurate

7:48 data that data is a source for Mar's

7:52 success so for Marvis you know one hand

7:55 is a lot of softwares infrastructure we

7:57 build on the cloud on top of that is

8:00 human human is you know data scientists

8:03 machine learning Engineers but on top of

8:05 that we also have a cloud infrastructure

8:08 engineers and the most important domain

8:10 experts and our AP Hardware switch and

8:15 SD van Hardware software Engineers

8:17 collectively work together to create

8:20 developed intelligence for the Maris so

8:23 you just kind of talked about all the

8:24 different teams that it takes to create

8:27 Marvis um can you give us a little bit

8:30 more about how it works from the AI

8:31 perspective so typically uh for Marvis

8:35 we start actually from a customer

8:37 support tickets so when customer we open

8:40 the support tickets we go through that

8:43 tickets with our C security team

8:46 identifies certain Cur tickets actually

8:49 the we could Marvis has already have an

8:51 answer for those Marvis does not have an

8:54 answer we work together with our domain

8:57 experts trying to identify that you

8:59 first of do we have the data in the

9:01 cloud can I capture that type of

9:04 behavior or problem second if we do then

9:08 you know we will have a multiple working

9:11 sessions with our domain experts the

9:14 working session of course we are data

9:16 science team and machine learning

9:17 Engineers working with the domain

9:19 experts we share our data analysis

9:22 results with them and the based on this

9:24 recommendation we continues to fine tune

9:27 our data analysis results and apply that

9:30 alysis apply to Mis Universe we

9:33 generally call the Mis Universe which

9:35 including our multiple Cloud production

9:38 environments to validate the results and

9:41 the based on the validation results then

9:44 we decide this is a um from machine

9:47 learning perspective this is a you know

9:49 the classification problem or is a

9:51 prediction problem then we can decide

9:54 what type of a machine learning

9:56 algorithms we want to use and on top of

9:58 that have to evaluate the you know Cloud

10:02 computation cost yeah and the latency

10:05 how how you know if we going to put in

10:08 the streaming or put in the platform the

10:11 batch process to do this to to implement

10:15 this use case how much of latency we can

10:18 tolerate and how much of the cloud cost

10:20 and of course most importantly is um and

10:24 how difficult to debug this issue if we

10:27 deploy in certain environments actually

10:30 the we may not be able to have access

10:33 like a go Cloud environments right so

10:36 based on this Factor we kind of finalize

10:39 our implementation then we deploy the

10:42 solution to the cloud then we are not

10:44 making customer faion we continuously

10:46 validate validate results with our

10:49 domain experts and you and also trying

10:52 to Val dat cases with friendly customers

10:56 until achieve certain confidence level

10:59 this solution can apply to Miss Universe

11:04 doesn't matter is gcp or in the databas

11:07 doesn't matter you retail customer or

11:09 university campuses we make sure our

11:12 solution is be able to adopt to

11:15 different environments then we'll make

11:17 it customer facing features so this kind

11:20 of general that the uh the development

11:24 cycle we have for any Mar Solutions so

11:27 it's not generally just a AIML about

11:30 data a lot of interactions with the

11:34 domain experts continues valid dat with

11:36 the customer use cases and their

11:39 specific environments so so this is kind

11:41 of the Journey of a mar is that how we

11:43 gain The Confidence from our customer

11:46 it's not just the data scientists that

11:48 qu the numbers get the statistical

11:50 results a lot of times we realize that

11:53 statistics accuracy may not represent

11:56 the real world uh to use cas two

11:59 scenarios so you just mentioned some

12:01 machine learning algorithms can you go

12:04 into how Marvis uses them for

12:05 troubleshooting so Marvis utiliz

12:08 multiple machine learning algorithms

12:10 like decision tree logistic regression

12:14 lstm and just a few right XG boosts and

12:18 the sharly those is all the different

12:20 techniques inside the ml Ai and so I can

12:24 give you specific one example like how

12:27 we utilize lstm that's uh you know

12:30 launch the neuron networks to do the

12:32 normal detection so why we want to use

12:36 the neuron Network such a complex

12:38 approach to do the normal detection the

12:40 reason is that we want to be able to

12:42 capture the seasonality think about it

12:44 if you're a um Camp University campuses

12:48 so during the weekend versus weekday

12:51 during the summer break versus normal

12:53 school hours the number of the Wi-Fi

12:57 devices is different so the behavior

13:00 activity on your campus is different in

13:02 order to identify a normal normally that

13:05 we need the machine learning model is

13:08 sophisticated enough to identify the

13:10 regular pattern you know because it's

13:13 weekend versus uh the during the normal

13:18 and school school days so that's why we

13:21 utilize the LM which also utilize more

13:25 than two months of data as a training

13:26 data so we can identify enough patterns

13:29 for your weekend resus day then we use

13:32 that model to continuous to predict

13:35 what's going to be the next hour that

13:37 the errors events occurred on your

13:41 network if anything is deviate from our

13:44 prediction we identify that as uh

13:47 normally so this kind of just you know

13:50 one of a um high level introduction

13:53 about how we want to use the STM to do

13:56 the normal detection so that when we

13:58 identify normal so we can report the

14:00 customer we can show the supporting

14:02 evidence based on your last six weeks of

14:04 data we can show you your behavior

14:07 pattern but as of today your behavior is

14:10 different so we have a timeline to show

14:12 you the supporting evidence to tell you

14:14 something not right on your network and

14:17 can we also doing the mutual information

14:20 identify this so normally mostly come

14:22 from the particular DHCP server or could

14:25 come from a

14:26 particular W lens so that save the time

14:29 for the customers administrators to

14:31 troubleshooting the issu they can just

14:33 we can pinpoint the specific domain of

14:36 the problem so it's easier for them to

14:39 find the root cause can you talk a

14:41 little bit about Marvis actions UI

14:44 otherwise known as the Beloved octopus

14:46 so yeah so I wish you we have a better

14:48 name for the octopus UI so yes that's a

14:51 my action page that's a key

14:54 differentiator u compare Juniper with

14:57 our competitors Marv action page provide

15:00 the highly focused view about the

15:04 customers Works what their top issues so

15:07 for their administrator they should just

15:10 look at the Marv action page to identify

15:12 the issues that they need to fix and

15:15 also in the same page we provide the

15:18 recommended actions and also supporting

15:21 evidence so from our Network customers

15:23 Network administrators they don't need

15:25 to do the debugging to S into the device

15:29 and uh capture the pcaps slicing dicing

15:32 the data to find the problem and uh you

15:35 know find the what's a problem what's

15:37 the solution we have the single point of

15:39 view given all the answers they need

15:42 second we reduce the noise we don't show

15:45 them tons of the graph tables nine

15:49 charts for them to compare the notes

15:52 here and also the answers we make sure

15:55 Mar setion is at the manageable level so

15:58 even though you manag you know thousands

16:00 of stores for retail customer versus uh

16:04 thousands of AP of the University campus

16:07 we make sure MAV action is only

16:09 manageable by the network

16:11 administrators so this is a key

16:13 differentiator we reduce the noise Make

16:16 the network administrators really focus

16:18 on the issues raised in the Marv action

16:21 page so that's a great example about how

16:24 visibility is such a big part of the

16:26 Marva solution so I want to say thank

16:28 thank you for joining us today um we

16:31 really appreciate all the knowledge that

16:33 you brought to us surely it's a great

16:35 pleasure to be here thank you Jessica so

16:38 and thank you for listening we look

16:40 forward to you joining us for our next

16:41  episode