(upbeat music)
>> Welcome back.
Day two of theCUBE's coverage of re:MARS in Las Vegas.
Amazon re:MARS, it's part of the Re Series
they call it at Amazon.
re:Invent is their big show,
re:Inforce is a security show,
re:MARS is the new emerging machine learning automation,
robotics, and space.
The confluence of machine learning
powering a new industrial age and inflection point.
I'm John Furrier, host of theCUBE.
We're here to break it down
for another wall to wall coverage.
We've got a great guest here,
CUBE alumni from our AWS startup showcase,
Krishna Gade, founder and CEO of fiddler.ai.
Welcome back to theCUBE.
Good to see you.
>> Great to see you, John.
>> In person.
We did the remote one before.
>> Absolutely, great to be here,
and I always love to be part of these interviews
and love to talk more about what we're doing.
>> Well, you guys have a lot of good street cred,
a lot of good word of mouth
around the quality of your product, the work you're doing.
I know a lot of folks that I admire and trust
in the AI machine learning area say great things about you.
A lot going on, you guys are growing companies.
So you're kind of like a startup on a rocket ship,
getting ready to go, pun intended here at the space event.
What's going on with you guys?
You're here.
Machine learning is the centerpiece of it.
Swami gave the keynote here at day two
and it really is an inflection point.
Machine learning is now ready, it's scaling,
and some of the examples that they were showing
with the workloads and the data sets
that they're tapping into, you know,
you've got CodeWhisperer, which they announced,
you've got trust and bias now being addressed,
we're hitting a level, a new level in ML,
ML operations, ML modeling, ML workloads for developers.
>> Yep, yep, absolutely.
You know, I think machine learning now
has become an operational software, right?
Like you know a lot of companies
are investing millions and billions of dollars
and creating teams
to operationalize machine learning based products.
And that's the exciting part.
I think the thing that that is very exciting for us
is like we are helping those teams
to observe how those machine learning applications
are working so that they can build trust into it.
Because I believe as Swami was alluding to this today,
without actually building trust into AI,
it's really hard to actually have your business users
use it in their business workflows.
And that's where we are excited about bringing their trust
and visibility factor into machine learning.
>> You know, a lot of us all know
what you guys are doing here in the ecosystem of AWS.
And now extending here, take a minute to explain
what Fiddler is doing for the folks that are in the space,
that are in discovery mode,
trying to understand who's got what,
because like Swami said on stage,
it's a full-time job to keep up
on all the machine learning activities
and tool sets and platforms.
Take a minute to explain what Fiddler's doing,
then we can get into some, some good questions.
>> Absolutely.
As the enterprise is taking
on operationalization of machine learning models,
one of the key problems that they run into
is lack of visibility into how those models perform.
You know, for example, let's say if I'm a bank,
I'm trying to introduce credit risk scoring models
using machine learning.
You know, how do I know when my model
is rejecting someone's loan?
You know, when my model is accepting someone's loan?
And why is it doing it?
And I think this is basically
what makes machine learning a complex thing
to implement and operationalize.
Without this visibility, you cannot build trust
and actually use it in your business.
With Fiddler, what we provide
is we actually open up this black box
and we help our customers to really understand
how those models work.
You know, for example, how is my model doing?
Is it accurately working or not?
You know, why is it actually
rejecting someone's loan application?
We provide these both fine grain
as well as coarse grain insights.
So our customers can actually deploy machine learning
in a safe and trustworthy manner.
>> Who is your customer?
Who you're targeting?
What persona is it, the data engineer,
is it data science, is it the CSO, is it all the above?
>> Yeah, our customer is the data scientist
and the machine learning engineer, right?
And we usually talk to teams
that have a few models running in production,
that's basically our sweet spot,
where they're trying to look for a single pane of glass
to see like what models are running in their production,
how they're performing,
how they're affecting their business metrics.
So we typically engage with like head of data science
or head of machine learning
that has a few machine learning engineers
and data scientists.
>> Okay, so those people that are watching,
you're into this, you can go check it out.
It's good to learn.
I want to get your thoughts on some trends
that I see emerging, and I want to get your reaction to those.
Number one, we're seeing the cloud scale now
and integration a big part of things.
So the time to value was brought up on stage today,
Swami kind of mentioned time to value,
showed some benchmark where they got four hours,
some other teams were doing eight weeks.
Where are we on the progression of value,
time to value, and on the scale side.
Can you scope that for me?
>> I mean, it depends, right?
You know, depending upon the company.
So for example, when we work with banks,
for them to time to operationalize a model
can take months actually,
because of all the regulatory procedures
that they have to go through.
You know, they have to get the models reviewed
by model validators, model risk management teams,
and then they audit those models,
they have to then ship those models
and constantly monitor them.
So it's a very long process for them.
And even for non-regulated sectors,
if you do not have the right tools and processes in place,
operationalizing machine learning models
can take a long time.
You know, with tools like Fiddler, what we are enabling
is we are basically compressing that life cycle.
We are helping them automate like model monitoring
and explainability
so that they can actually ship models more faster.
Like you get like velocity in terms of shipping models.
For example, one of the growing fintech companies
that started with us last year
started with six models in production,
now they're running about 36 models in production.
So it's within a year,
they were able to like grow like 10x.
So that is basically what we are trying to do.
>> At other things, we at re:MARS, so first of all,
you got a great product and a lot of markets that grow onto,
but here you got space.
I mean, anyone who's coming out of college
or university PhD program,
and if they're into aero,
they're going to be here, right?
This is where they are.
Now you have a new core companies with machine learning,
not just the engineering that you see in the space
or aerospace area, you have a new engineering.
Now I go back to the old days where my parents,
there was Fortran, you used Fortran was Lingua Franca
to manage the equipment.
Little throwback to the old school.
But now machine learning is companion,
first class citizen, to the hardware.
And in fact, and some will say more important.
>> Yep, I mean, machine learning model
is the new software artifact.
It is going into production in a big way.
And I think it has two different things
that compare to traditional software.
Number one, unlike traditional software, it's a black box.
You cannot read up a machine learning model score
and see why it's making those predictions.
Number two, it's a stochastic entity.
What that means is it's predictive power
can wane over time.
So it needs to be constantly monitored
and then constantly refreshed
so that it's actually working in tech.
So those are the two main things you need to take care.
And if you can do that,
then machine learning can give you a huge amount of ROI.
>> There is some practitioner kind of like craft to it.
>> Correct. >> As you said,
you got to know when to refresh,
what data sets to bring in, which to stay away from,
certainly when you get to the bias,
but I'll get to that in a second.
My next question is really along the lines of software.
So if you believe that open source
will dominate the software business, which I do,
I mean, most people won't argue.
I think you would agree with that, right?
Open source is driving everything.
If everything's open source,
where's the differentiation coming from?
So if I'm a startup entrepreneur
or I'm a project manager working
on the next Artemis mission, I got to open source.
Okay, there's definitely security issues here.
I don't want to talk about shift left right now,
but like, okay, open source is everything.
Where's the differentiation,
where do I have the proprietary edge?
>> It's a great question, right?
So I used to work in tech companies before Fiddler.
You know, when I used to work at Facebook,
we would build everything in house.
We would not even use a lot of open source software.
So there are companies like that
that build everything in house.
And then I also worked at companies
like Twitter and Pinterest,
which are actually used a lot of open source, right?
So now, like the thing is,
it depends on the maturity of the organization.
So if you're a Facebook or a Google,
you can build a lot of things in house.
Then if you're like a modern tech company,
you would probably leverage open source,
but there are lots of other companies in the world
that still don't have the talent pool
to actually build, take things from open source
and productionize it.
And that's where the opportunity for startups comes in
so that we can commercialize these things,
create a great enterprise experience,
so actually operationalize things for them
so that they don't have to do it in house for them.
And that's the advantage working with startups.
>> I don't want to get all operating systems with you
on theory here on the stage here,
but I will have to ask you the next question,
which I totally agree with you, by the way,
that's the way to go.
There's not a lot of people out there that are peaked.
And that's just statistical and it'll get better.
Data engineering is really narrow.
That is like the SRE of data.
That's a new role emerging.
Okay, all the things are happening.
So if open source is there, integration is a huge deal.
And you start to see the rise of a lot of MSPs,
managed service providers.
I run Kubernetes clusters,
I do this, that, and the other thing.
So what's your reaction to the growth
of the integration side of the business
and this role of new services coming from third parties?
>> Yeah, absolutely.
I think one of the big challenges
for a chief data officer or someone like a CTO
is how do they devise this infrastructure architecture
and with components, either homegrown components
or open source components or some vendor components,
and how do they integrate?
You know, when I used to run data engineering at Pinterest,
we had to devise a data architecture
combining all of these things
and create something that actually flows very nicely, right?
>> If you didn't do it right, it would break.
>> Absolutely.
And this is why it's important for us, like at Fiddler,
to really make sure that Fiddler can integrate
to all varies of ML platforms.
Today, a lot of our customers use machine learning,
build machine learning models on SageMaker.
So Fiddler nicely integrate with SageMaker
so that data, they get a seamless experience
to monitor their models.
>> Yeah, I mean, this might not be the right words for it,
but I think data engineering as a service
is really what I see you guys doing,
as well other things, you're providing all that.
>> And ML engineering as a service.
>> ML engineering as a-
Well it's hard.
I mean, it's like the hard stuff.
>> Yeah, yeah.
>> Hear, hear.
But that has to enable.
So you as a business entrepreneur,
you have to create a multiple of value proposition
to your customers.
What's your vision on that?
What is that value?
It has to be a multiple, at least 5 to 10.
>> I mean, the value is simple, right?
You know, if you have to operationize machine learning,
you need visibility into how these things work.
You know, if you're CTO or like chief data officer
is asking how is my model working
and how is it affecting my business?
You need to be able to show them a dashboard,
how it's working, right?
And so like a data scientist today struggles to do this.
They have to manually generate a report,
manually do this analysis.
What Fiddler is doing them is basically reducing their work
so that they can automate these things
and they can still focus on the core aspect
of model building and data preparation
and this boring aspect of monitoring the model
and creating reports around the models
is automated for them.
>> Yeah, you guys got a great business.
I think it's a lot of great future there
and it's only going to get bigger.
Again, the TAM's going to expand
as the growth rising tide comes in.
I want to ask you on while we're on that topic
of rising tides, Dave Malik and I,
since re:Invent last year have been kind of kicked down
around this term that we made up called supercloud.
And supercloud was a word that came out of these clouds
that were not Amazon hyperscalers.
So Snowflake, Buildman Sachs, Capital One, you name it,
they're building massive proprietary value
on top of the CapEx of Amazon.
Jerry Chen at Greylock calls it castles in the cloud.
You can create these moats.
>> Yeah, right.
>> So this is a phenomenon, right?
And you land on one, and then you go to the others.
So the strategies, everyone goes to Amazon first,
and then hits Azure and GCP.
That then creates this kind of multicloud so, okay,
so super cloud's kind of happening, it's a thing.
Charles Fitzgerald will disagree,
he's a platformer, he says he's against the term.
I get why, but he's off base a little.
We can't wait to debate him on that.
So superclouds are happening,
but now what do I do about multicloud,
because now I understand multicloud,
I have this on that cloud,
integrating across clouds is a very difficult thing.
>> Krishna: Right, right, right.
>> If I'm Snowflake or whatever, hey, I'll go to Azure,
more TAM expansion, more market.
But are people actually working together?
Are we there yet?
Where it's like, okay,
I'm going to re-operationalize this code base over here.
>> I mean, the reality of it,
enterprise wants optionality, right?
I think they don't want to be locked in
into one particular cloud vendor on one particular software.
And therefore you actually have in a situation
where you have a multicloud scenario
where they want to have some workloads in Amazon,
some workloads in Azure.
And this is an opportunity for startups like us
because we are cloud agnostic.
We can monitor models wherever you have.
So this is where a lot of our customers,
they have some of their models are running
in their data centers
and some of their models running in Amazon.
And so we can provide
a universal single pan of glass, right?
So we can basically connect all of those data
and actually showcase.
I think this is an opportunity for startups
to combine the data streams come
from various different clouds
and give them a single pain of experience.
That way, the sort of the where is your data,
where are my models running, which cloud are there,
is all abstracted out from the customer.
Because at the end of the day,
enterprises will want optionality.
And we are in this multicloud.
>> Yeah, I mean, this reminds me of the interoperability days
back when I was growing into the business.
Everything was interoperability
and OSI and the standards came out,
but what's your opinion on openness, okay?
There's a kneejerk reaction right now in the market
to go silo on your data
for governance or whatever reasons,
but yet machine learning gurus and experts will say,
"Hey, you want to horizon horizontal scalability
and have the best machine learning models,
you've got to have access to data and fast
in real time or near real time."
And the antithesis is siloing.
>> Krishna: Right, right, right.
>> So what's the solution?
Customers control the data plane
and have a control plane that's...
What do customers do?
It's a big challenge.
>> Yeah, absolutely.
I think there are multiple different architectures
of ML, right, you know?
We've seen like where vendors like us
used to deploy completely on-prem, right?
And they still do it, we still do it in some customers.
And then you had this managed cloud experience
where you just abstract out the entire operations
from the customer.
And then now you have this hybrid experience
where you split the control plane and data plane.
So you preserve the privacy of the customer
from the data perspective,
but you still control the infrastructure, right?
I don't think there's a right answer.
It depends on the product that you're trying to solve.
You know, Databricks is able to solve this control plane,
data plane split really well.
I've seen some other tools
that have not done this really well.
So I think it all depends upon-
>> What about Snowflake?
I think they a-
>> Sorry, correct.
They have a managed cloud service, right?
So predominantly that's their business.
So I think it all depends on what is your go to market?
You know, which customers you're talking to?
You know, what's your product architecture look like?
You know, from Fiddler's perspective today,
we actually have chosen, we either go completely on-prem
or we basically provide a managed cloud service
and that's actually simpler for us instead of splitting-
>> John: So it's customer choice.
>> Exactly.
>> That's your position. >> Exactly.
>> Whoever you want to use Fiddler, go on-prem,
no problem, or cloud. >> Correct, or cloud, yeah.
>> You'll deploy and you'll work across
whatever observability space you want to.
>> That's right, that's right.
>> Okay, yeah.
So that's the big challenge, all right.
What's the big observation from your standpoint?
You've been on the hyperscaler side, your journey,
Facebook, Pinterest, so back then you built everything,
because no one else had software for you,
but now everybody wants to be a hyperscaler,
but there's a huge CapEx advantage.
What should someone do?
If you're a big enterprise,
obviously I could be a big insurance,
I could be financial services, oil and gas,
whatever vertical, I want a supercloud, what do I do?
>> I think like the biggest advantage enterprise today
have is they have a plethora of tools.
You know, when I used to work on machine learning
way back in Microsoft on Bing Search,
we had to build everything.
You know, from like training platforms,
deployment platforms, experimentation platforms.
You know, how do we monitor those models?
You know, everything has to be homegrown, right?
A lot of open source also did not exist at the time.
Today, the enterprise has this advantage,
they're sitting on this gold mine of tools.
You know, obviously there's probably a little bit
of tool fatigue as well.
You know, which tools to select?
>> There's plenty of tools available.
>> Exactly, right?
And then there's like services available for you.
So now you need to make like smarter choices
to cobble together this,
to create like a workflow for your engineers.
And you can really get started quite fast,
and actually get on par
with some of these modern tech companies.
And that is the advantage that a lot of enterprises see.
>> If you were going to be the CTO
or CEO of a big transformation, knowing what you know,
'cause you just brought up the killer point
about why it's such a great time right now,
you got platform as a service
and the tooling essentially reset everything.
So if you're going to throw everything out and start fresh,
you're basically brewing the system architecture.
It's a complete reset.
That's doable.
How fast do you think you could do that
for say a large enterprise?
>> See, I think if you set aside the organization processes
and whatever kind of comes in the friction,
from a technology perspective, it's pretty fast, right?
You can devise a data architecture today
with like tools like Kafka, Snowflake and Redshift,
and you can actually devise a data architecture
very clearly right from day one
and actually implement it at scale.
And then once you have accumulated enough data
and you can extract more value from it,
you can go and implement your MLOps workflow
as well on top of it.
And I think this is where tools like Fiddler
can help as well.
So I would start with looking at data,
do we have centralization of data?
Do we have like governance around data?
Do we have analytics around data?
And then kind of get into machine learning operations.
>> Krishna, always great to have you on theCUBE.
You're great masterclass guest.
Obviously great success in your company.
Been there, done that, and doing it again.
I got to ask you,
since you just brought that up about the whole reset,
what is the superhero persona right now?
Because it used to be the full stack developer, you know?
And then it's like, then I call them,
it didn't go over very well in theCUBE,
the half stack developer,
because nobody wants to be a half stack anything,
a half sounds bad, worse than full.
But cloud is essentially half a stack.
I mean, you got infrastructure, you got tools.
Now you're talking about a persona that's going to reset,
look at tools, make selections, build an architecture,
build an operating environment,
distributed computing operating.
Who is that person?
What's that persona look like?
>> I mean, I think the superhero persona today
is ML engineering.
I'm usually surprised how much is put on an ML engineer
to do actually these days.
You know, when I entered the industry
as a software engineer, I had three or four things
in my job to do, I write code, I test it,
I deploy it, I'm done.
Like today as an ML engineer, I need to worry about my data.
How do I collect it?
I need to clean the data, I need to train my models,
I need to experiment with what it is, and to deploy them,
I need to make sure that they're working
once they're deployed.
>> Now you got to do all the DevOps behind it.
>> And all the DevOps behind it.
And so I'm like working halftime as a data scientist,
halftime as a software engineer,
halftime as like a DevOps cloud.
>> Cloud architect.
>> It's like a heroic job.
And I think this is why this is why obviously these jobs
are like now really hard jobs and people want to be more
and more machine learning >> And they get paid.
>> engineering. >> Commensurate with the-
>> And they're paid commensurately as well.
And this is where I think an opportunity
for tools like Fiddler exists as well
because we can help those ML engineers do their jobs better.
>> Thanks for coming on theCUBE.
Great to see you.
We're here at re:MARS.
And great to see you again.
And congratulations on being on the AWS startup showcase
that we're in year two, episode four, coming up.
We'll have to have you back on.
Krishna, great to see you.
Thanks for coming on.
Okay, This is theCUBE's coverage here at re:MARS.
I'm John Furrier, bringing all the signal
from all the noise here.
Not a lot of noise at this event, it's very small,
very intimate, a little bit different,
but all on point with space, machine learning,
robotics, the future of industrial.
We'll back with more coverage after the short break.
>> Man: Thank you John.
(upbeat music)