>> From theCUBE Studios in Palo Alto in Boston,
connecting with thought leaders all around the world.
This is a CUBE conversation.
>> Hi, I'm Stuart Miniman.
and welcome to this special CUBE conversation.
Always love when we get to talk to founders of companies,
when they're drilling into some interesting technologies.
I want to welcome a new guest to theCUBE
as well as one of our CUBE alumni,
sitting right next to me on the screen.
First of all, we have Martin Casado,
who is a general partner within Andreessen Horowitz.
Martin great to see you.
>> Its great to be here.
>> And you've brought along Mike Del Balso also who is the
co founder and CEO of Tecton recently out of stealth
going to dig into a lot of the ML discussion.
Mike, thanks for joining us.
>> Thanks for having me on.
Alright, so Martin look,
you're no stranger to being a founder yourself,
we've loved having you on theCUBE over the years.
I have to get since we're getting you on here in 2020,
we of course need to start
with the fact that there's a global pandemic going on.
and I'm curious from our standpoint,
from an investment standpoint and looking at technology
How does this make it a little bit different in 2020,
say than you would've thought coming into the year?
>> Yeah, so I think there's kind of a near term answer
and a long term answer.
I think the near term answer is
people don't really understand what
the broad impact is going to be.
And so companies in the portfolio
and the guidance that we do is to
be conservative with cash.
let's see how Q2 plays out and then let's figure out
the right way to kind of operate the company
in light of the macro changes.
Long term however, it's very clear that
every digital transformation project right now
is being fast tracked.
And as a result we think it's a huge boom to infrastructure.
And as who been for the software. Right?
like where in the past you could deal with
kind of legacy setups that were on print.
This is just not the case anymore.
So take for a company like Tecton, Mike's company,
like there's a lot of conversations that happen now.
where the company is like, wow, we really need to have
our infrastructure digitized
and it all needs to be in the cloud,
and all need to be remote and so forth.
So we're actually seeing a ton of tailwinds
even though there's uncertainty
on the macro environment in the near term.
>> Yeah. You make some great points, Martin absolutely.
The companies that have actually gone through
some digital transformation, the goals of that is
number one I should be data-driven,
number two I should be able to be much more agile.
And that's what we need in uncertain times
is to be able to react fast and answer it.
Mike unfortunately I've talked to plenty of companies,
you can't necessarily choose when's the right time
to launch a company.
When's the right time to do an IPO,
trying to time the market.
But sorry to say interesting times are upon us.
So let's talk a bit about Tecton,
give us a little bit of your background
the team, the core team
I believe coming out of Uber with the Michelangelo project
that led to Tecton?
>> Yeah. Great. So at Tecton we really focus on
what we call operational machine learning,
which is really about helping organizations
really use machine learning and applied context,
really powering customer experiences,
powering business processes,
things that really make it to production.
And so we help these machine learning, AI efforts
get past the finish line.
And a little bit about the background of me,
I used to work at Google as a product manager for the
machine learning teams that power the ads auction.
So the models that choose which ads to show.
and run in real time and are highly productionized.
and are really core to the business.
And then I was at Uber after that
and Uber helped start
their first centralized machine learning team.
And it was really the whole journey for Uber going from
just starting to getting to
tens of thousands of models in production.
And a big component of that was
a lot of the technology that we built there,
the platforms and infrastructure that we built
to support the different business teams.
To be able to embed machine learning and AI products.
And so what we're talking about,
all these very applied use cases
real time, fraud detection, ETA estimation, search pricing.
All these things, when you think about with Uber.
so through that journey of supporting and helping them
get to 100 with machine learning.
We built out this platform called Michelangelo,
which is really a machine learning platform.
It's really an end to end machine learning platform.
Learned a lot of lessons as we helped out, dozens of teams.
go through the full life cycle,
start with starting a project pay. What is this,
what does this mean? What does my business problem,
how does it translate to a machine learning problem
all the way to having a model in production monitored,
and really fully productionized and kind of
a growing core to that business.
So we learned a lot of lessons from building that at Tecton.
My co-founders are the other leaders of that project
and we learned a lot of really important lessons that
lead to the success of these machine learning projects
and we're now focused on helping
a lot of other organizations
really start up their machine learning efforts
and get these things into production.
>> Yeah Martin, maybe you could give us
a little bit of context here.
When I think about repeatability of solutions,
how much they scale,
there's only so many Google's and Uber's out there.
when I look back at the big data world,
there wasn't a lot of repeatability,
it seemed like everything was custom.
What did you see with Tecton?
What are you looking at in the ML space
that made them such an attractive investment?
>> Sure. So maybe let's just pull back and talk about
what's going on in systems and infrastructure in general.
And I actually think this is probably the biggest shift.
Certainly I've seen in my career which is,
it used to be, if you looked at a system,
let's say a super but whatever system,
the correctness of that system.
and the performance of that system
and the compliance of that system,
and the security was dictated
by the code that you wrote, right?
You wrote bad code you made bugs,
you had vulnerabilities in your code
that would dictate the system.
But more and more that's actually not the case.
I mean these days kind of performance, accuracy,
security compliance is actually dictated
by the data that you feed into. Right?
You create these models,
you feed the data models, the data gives you output
and the data that you feed in and like
your work flows around those models
who are really dictating things like pricing
or things like fraud, these really important things.
And unlike code,
we don't have the tools to manage data in the same way.
And so if you think of it
we're moving kind of from this code economy,
to this data economy more and more techniques to correct
dictates the correctness of all of these systems.
and we're talking about trillions of dollars a month.
But if you actually look at the tooling around it,
it still feels like the seventies around code,
which is like you've got fiefdoms
and you've got a lot of tribal knowledge.
And so we've been tracking this trend for a long time.
We're investors in Databricks
We've got a large data portfolio.
I mean, it's very obvious if you look at
what's happening with the cloud data warehouses,
if you think like Redshift big query and stuff.
The world is going data extracting information out of data.
And so on the backdrop of that, we're like okay,
you need to be able to think of data like you think of code.
and have the tooling around it that helped makes
the lives of people working with this stuff simpler,
especially for the core use cases which is ML and AI.
And to that end I think that this is
probably known in the industry
but like looking in the leading companies
is like a crystal ball into the future, right?
Because they tackle a lot of the problems
before the rest of the industry did.
And Michelangelo was very well know
as the leading project in this.
It had a broad set of respect from the community
and kind of created this notion of a feature store
which has now been replicated.
And so really this is like the preeminent project
in one of the biggest macro transformations.
Beyond that, we met the team that are fantastic.
We've got great chemistry,
we've got a lot of similar backgrounds.
And so the investment was pretty straight forward from that.
But I do think it's important to frame it
in the context of this macro shift that's going on.
>> Yeah. it can't be overstated how important data is.
I do think we need a new analogy probably
with what happened with the global pandemic.
Everybody was talking about data being the new oil
and oil is pretty deep right now.
And data is definitely not losing its value.
Mike, when I read some of the discussion
about Tecton enables data scientists
turn raw data into production ready features
and predictive singles as signals
it sounds really impressive.
So help us understand kind of the core thing that you do
and where we are in the product life cycle.
>> Great. Well so a machine learning application
there's fundamentally two components. Right?
There's a model that you have to build
that's going to make the decisions,
given a certain set of inputs.
And then there's the features
which ended up being those inputs.
that the model uses making decision.
common machine learning infrastructure stats,
really are split into two layers.
There's a model management layer and
a feature management layer.
And that's an emerging pattern in some of the more
sophisticated machine learning. stacks that are out there.
And what we build at Michelangelo we really had
this model management layer, this feature management layer,
and we recognize that that feature management layer
was the thing that really allowed us to go from
not just zero to one, but one to end
and scale out machine learning across
a number of different used cases
and allow individual data scientists to own
more than just one model in production.
And so really what's at the core of that is
a few components.
The first is just feature pipelines.
So these are data pipelines that plug into the businesses
raw data via batch streaming, real time data
and turn those into features
that are these predictive signals and models consume.
The second part of that is a feature store,
which catalogs these feature transformations,
catalog these pipelines and draws,
the output raw data or the output feature data.
And then the third component is feature service.
Making those features accessible to a data scientist
when they're building their models.
And to the models in the production environment
so they can make these decisions
sometimes needed in milliseconds
for real time decisioning that is quite common.
and a lot of high value machine learning applications.
what Tecton really is,
it's a data platform for machine learning
that manages all the feature data
and feature transformations that allow an organization
to share the predictive signals.
These features across use cases in reading catalog needs
and understand what they are.
And secondly get these into production
so they don't get hung up in that final stage
right before they're trying to cross
the finish line with the machine learning project.
>> Stuart: All right.
And Mike the product today,
my understanding of private beta.
You do have some customers at that point,
tell us a little bit about that.
>> Yeah, we're at private beta with a number of customers.
We just went into full production with it last month.
A couple of other customers that
I maybe shouldn't name on the air,
but we are spending time engaging in
kind of like deep hands-on engagements.
with different teams who are really trying
to set up their machine learning on the cloud.
Figuring out how to get their
machine learning in production.
And it tends to be teams that are trying to really use
machine learning for operational use cases.
Really trying to drive real business decisions
and power their product customer experiences.
And not as much as a lot of the kind of like
research algorithm research stuff,
but we're really just trying to solve
these core data problems
that are preventing machine learning projects
from being successful.
>> Yeah. And it was interesting Martin.
I was listening to some of what Mike was saying
I'm like, okay.
It's not quite the analogy of micro-segmentation.
or separating the control plane or
the network plane and networking,
but there were some analogies there.
What I want to ask you though is
the role of data?
I talked to Andy Jassy a couple of years ago.
I asked him the flywheel for AWS for years was customers.
How many customers they could get and I was wondering
does data become that new flywheel?
And there's the center of gravity's and the
customers that can happen and monetize with going there.
So I'm just curious your thoughts on that.
>> So I think people don't appreciate
how different data is than code.
And so I just want to start there because
I think it's really germane to this topic.
So listen code is like a finite state. Right?
It's like, it's lines of code.
You can build it, you can modularize it,
It's like building a house.
And so the tools that you put around code kind of reign in,
what's already a fairly low entropy system,
like a fairly orderly system.
Data On the other hand, data is like the natural world.
It's all of the complexity of the universe. Right?
It's the behavior of humans.
It's temperature readings
and there's so much more complexity.
and there's so much more entropy in data
that the way that you deal with it is so fundamentally
different than you have to deal with code.
And so we've all of these and so I wanted to start that with
we've heard all of these analogies about data,
data is the new oil data is for the value,
et cetera, et cetera, et cetera.
But a lot of it's tautological,
meaning yes, of course there's value in data. Yeah.
Yes. If you have proprietary access to data,
you've got proprietary access to data.
But what we don't really know is
how do you take data and reign it in?
So you can use it in the same way
that you use software system.
We actually don't even know how to do that
and so talking about things like
data network effects and extracting data
is a little bit preliminary because
we still actually don't even understand,
like how much work it takes to mine insights from data.
What I do know you need,
I do know you need the tools to do it
and I do know that those tools are quite different.
and so I think that we're now in this era
building the tooling that is required
to extract the insights of that data.
And I think that's a very necessary step
and this is where a Tecton comes in,
to provide that tooling.
And I think once we have a better handle on that
then we can start asking these deeper questions,
which I think are great questions.
But the things like how defensible is data?
Do you have network effects of the data?
can you put in a finite amount of effort
and extract signal at all times?
Like how messy is data, et cetera.
So I think that's kind of where we are in this journey,
which is exactly why you need companies like Tecton
to help answer.
>> Alright. So Mike there's been the promise of
really unlocking data now.
There has been a really interesting discussion point
for the last five or 10 years.
The company is named Tecton,
I've read some of the blog posts
and talk about the Cambrian explosion and changes there.
So give us if we're looking forward,
you've just come out of stealth.
What is success for Tecton two to three years out from now?
>> Yeah, I think the biggest thing is
we're trying to help organizations.
Recognize that their data really is an asset
and treat their features like assets.
And when we can get to a point where organizations that
teams that want to use machine learning
and production don't need to throw
a million data engineers at a problem.
And we get added to a point where machine learning is not,
a special team of experts that are super expensive that
you kind of leave in the corner of your building
and hope they come back 18 months later
with some project that is showing some value,
that would be success for us.
we really are dead focused on the problems
that are preventing these projects
just from getting into production.
And when we see the industry as a whole have seen success
with these machine learning projects,
I think we will have our mission accomplished.
>> All right, Martin, I'll give you the final word to
the opportunity you see in front of Tecton.
>> I honestly think the data industry is going to be
10 X the computer industry.
I just think like with compute
you're building houses from the ground up
and there's a ton of value there.
I think with data is you're extracting insight
and value from the universe, right?
It's like the natural system.
And every company has data and lots of data
and all of it has some information.
And so I think that this is a chance to be
a very, very pivotal company.
in democratizing access to data.
So I think that the opportunity is enormous.
>> Well, Martin, thank you for joining us
again on the update, Mike, thank you
Welcome to being a CUBE alum.
Definitely hope to have you back soon to track the journey,
congrats on step one out the door
and best of luck going forward.
>> Thank you.
>> That's great. Thanks too
>> All right. Be sure to check out the cube.net.
for the upcoming events that we have
today they're all virtual, but the interviews are all there
as well as all the archive.
I'm Stuart Miniman and thank you for watching theCUBE.
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