[MUSIC PLAYING]
FILLIPO MADELLA: Hi, welcome to Architecting with Google Cloud.
My name is Fillipo Madella how many times
you're headed it to your favorite
or need a dining spot that you probably were in a rush
for an event which was about to start or, like myself,
had hungry kids running around and just wish the line wait
time wasn't that long.
I guess far too often, right.
So what if there was a way to get in a priority queue
to avoid those long lines and have a better experience?
Well, there is a way.
And today I invited Gaurav Aggarwal--
he's the co-founder of Sleek--
to share a little bit more about its AI and ML-based priority
lane solution that retailers can implement
in just one line of code and consumers
like us can enjoy the shorter wait.
Welcome Gaurav to the show.
GAURAV AGGARWAL: Hey, Fillipo.
Great to be here.
Thanks for inviting me.
Yeah, really excited to be here.
FILLIPO MADELLA: Thank you.
Wow, it's our pleasure.
And it seems less time in line sounds like an amazing idea.
So tell us a little bit more about Sleek, how it works,
and how these actually idea came about for you.
GAURAV AGGARWAL: Definitely.
Sleek is about giving you your time back.
Imagine going to an event and not having to wait in line.
You know, going to the Indianas game
and not having to wait in line to get in the event
and getting to your seat, getting
a full drink, your favorite merch,
or getting out of the event.
That is what we're trying to do.
We're trying to eliminate line anywhere and everywhere.
It need not be stadium.
It can be your favorite restaurant or your salon,
and any place where there are products
and services to be sold.
And you can actually get your time back.
You can just reserve your slot for your product or service
and get back when it's your time.
Or you can pay a premium your fee
and take a Disney fast pass, have a priority service
if you like.
FILLIPO MADELLA: Right.
Very interesting.
So I live in Texas.
And what if I owned this very popular taco truck?
What do I need to do to actually implement
Sleek solutions to make my customers'
experience a little bit better?
AGGARWAL: So we spend a lot of time
to make sure it is seamless and easy for our vendors
to onboard on a system because we
are trying to give them as much ease of use as possible.
For you to go to onboard our system,
you can sign up on a website or you
can log-in with an official partner like [INAUDIBLE]..
When you sign up, you'll get an email.
And in the email, you'll have a link.
The link very similar to-- once you click on the link,
you'll see a permission page very similar to what
you see when you sell an Android app
or when you click on a Facebook app, and these sort of things.
You log in with your POS credentials.
So POS mean Square, Clover, Toast, these kind
of point of sale systems.
And just give us permission.
And that's it.
Voila, we're in the system.
We get all the historical data.
We can predict how long it takes you
to make the burrito, and all sorts of stuff.
We will generate a QR code and a unique link
for your restaurant or your place.
And you can place it on a website, print it out.
And we'll definitely send you marketing material for that,
to give you an exposure that you can use, and even
put on Google Maps.
FILLIPO MADELLA: OK, it seems very
straightforward and easy to implement.
And you mentioned a few use cases outside
of this restaurant industry.
Right?
So what other meaningful use cases have you seen,
like, Slick Solutions being applied for recently?
GAURAV AGGARWAL: A use case that I'm very excited about
and I can personally relate to is--
even you could, because you work at Google,
you have these fancy cafeterias, which have very nice food,
but it's always crowded.
And we are working with our channel partners
to power some of these commercial catering,
in some way.
So as an employee, you can just reserve your meal.
You can say, hey, I want a burrito and blah, blah, blah.
And then I can just place the order.
And you'll get a text when you can just go pick it up.
So you need not physically wait in line and kind
of waste your time.
You can enjoy your time with your colleagues
and do whatever you want.
So it just saves time for you, gives you
like a fast pass, Disney Pass kind of service,
and gives your time back.
FILLIPO MADELLA: Thank you.
To help us further with our architecture conversation,
tell us a little about the scale of your operations today
and how many customers usually are using your solutions
on a daily basis?
GAURAV AGGARWAL: So we are covering about 18,000 food
trucks via our partnership with our channel partner
[INAUDIBLE].
And 18,000 is a very significant portion of the food industry
in the United States.
FILLIPO MADELLA: Well, that seems a lot to handle daily.
So let's then start talking a little bit
about your infrastructure setup?
And how do you set up your architecture stack today
to reach such scale on an ongoing basis?
GAURAV AGGARWAL: So to give you some context,
we are a very small team.
We have 10 employees across engineering and other aspects.
And we are, like, five engineers.
So we are a very small team.
And to handle at such a large scale,
we cannot do everything--
DevOps, provisioning, security, there
is just like so many things going on.
And we [INAUDIBLE] to focus on your business.
That's the kind of philosophy that we have been following.
And that is one of the reasons we chose Kubernetes.
It takes a lot of complexity out of the system for us
so we can kind of focus on our business
logic and our core value proposition, which
is wait time and development.
So we use Kubernetes.
And Kubernetes takes care of all the complexities like
networking, site creation set-up, load balancing,
and all those--
the mission-critical thing that you definitely
need in place and you want to rely on.
And that's what Kubernetes provides us.
All of our business logic sits as, like, a stateless pod.
So [INAUDIBLE] Kubernetes basically a docker image
that we kind of publish.
And it is used in the Kubernetes as a pod.
We can easily horizontally scale.
Like if we go from 18,000 to, like, 1 million,
we can begin throwing in more parts,
and we can scale very easily.
That's the benefit of Kubernetes.
And, actually, we saved close to $100,000
from Kubernetes in the last year or so,
which has been great for us as a small salary.
Free money, it's always good.
And as a philosophy, we are trying to cut down, as much as
possible, the DevOps.
As I said, we are using managed services.
That's where GKE comes in and plays a great role for us.
All the complexity is taken care of.
It's a first grade server within GCP.
We can integrate from our other top provider, AWS.
So we were in AWS before moving to GCP.
And some of our data is still on AWS.
Like, there is a universe with Cloud, Cloud for AWS, as a CBM.
And it works seamlessly with our pods.
And MongoDB is the database.
So I would say very scalable stack powered by multi-clouds.
And yeah, it works for us right now.
FILLIPO MADELLA: You mentioned a little bit
of your recent migration to GCP.
So what were the main technical driving decisions that
let you to make that decision?
GAURAV AGGARWAL: So we were using Kubernetes on AWS,
like we are using here.
And we started using AWS because of different reasons.
We were using AWS when we were not using Kubernetes, when
we were in [INAUDIBLE] stage.
Somehow, Kubernetes was still, I would say,
getting into the AWS space.
A lot of things were missing.
We had to use third party services
like Helm and Lens, which is doable
when you are using massive other aspects like the MongoDB.
But when you are using AWS for just
Kubernetes and a few other things,
it seemed a bit overkill.
And that's where we started exploring other aspects
like GCP, Azure [INAUDIBLE].
And using GCP, it's very native.
Everything is kind of part of GKE.
I rarely would have to use Lens or Helm,
and these custom installing systems and such.
Either I can do it from the Google Cloud console
or like the [INAUDIBLE].
It works really well for us.
And that was one of the main reasons we moved.
And obviously, Google has its own benefit
of deep learning VMs.
And Google is known for its own APIs.
We are using Google Computer Vision APIs.
So that was also a motivation for us.
FILLIPO MADELLA: Looking at your infrastructure,
a lot of Sleek's magic here comes
from the intelligent infrastructure that you have
and the data you have running on the edge.
So could you talk a little bit about the edge component
of your infrastructure?
GAURAV AGGARWAL: Definitely.
So I just want to highlight, why do we need each component?
Why can't we just, you know, get data from point of the system,
and that should be enough?
So with point is the system, you get historical data.
So we understand that launch is going to be a busy time,
Friday evenings are a busy time.
But there are these anomalous events,
like when you're in a stadium, you
don't know when the game is going to be scheduled.
It can be on the weekend, but time can vary.
If you're a bar in front of the stadium, you will have,
once the game is over, a lot of people come in.
And so you need to model this thing.
And we cannot ignore these spikes because we are
in the business of giving the right wait time most
of the time.
Although we can just say, 10 minutes wait time on average,
and you should be happy.
That's not how it works for us.
We need to be very accurate.
That's what we need to do to figure out wait time on ground,
how many people are there?
And we have tried computer vision.
We use state of the art [INAUDIBLE] computer models.
And we wanted to be able to do better than that.
But the privacy was a concern.
So we ended up using a very proprietary technology,
where it's like a size of, like, an iPhone,
where we have this thing where you can plug in.
It detects how many people are in the vicinity.
And it can make a differentiation
in terms of how many of these people
are employees, how many are the customers potentially,
or how long the people are there.
So we can get an idea of the foot traffic.
And that kind of gives us the real-time idea of,
OK, there's [INAUDIBLE] people walked in,
and those sort of things.
And we feed all this data in S3, for our AWS server
for long-term storage, again, because we
were on AWS back then.
But are currently being migrated to DCP4
for our modeling purposes.
FILLIPO MADELLA: So you are leveraging machine learning
models to create a lot of these priority cues and predictions.
So could you expand a little bit more
on how you designed this ML pipeline?
GAURAV AGGARWAL: Sure.
So we have these small IT devices,
which gathers data and sends this anonymous data
to our services.
So two types of data sets are sent to us.
One type of data set is the real-time, that kind of
feeds into our system.
And it is directly sent out to our services running in GKE.
So it kind of gives an idea, aggregated, of how many people
are there at time T, and those sort of things.
More nuanced data, still private, and fairly sensitive
data, that we do remove any kind of identifiers and such things.
It's sent and it lies in the S3 bucket for us.
And it is for archival storage.
Whenever we want to update the model,
we move the data to Cloud Storage, which is on GCP.
GCP provides a very easy-to-move data, scripts,
and pipeline for us.
So we just use official stuff for that.
We use GPC, GP-powered deep learning VMs with PyTorch,
and train them all on these VMs.
And you know how the model training works.
We train different versions of the model
and post the model configuration to GitHub, to docker, and then
kind of [INAUDIBLE] on a flight experiment.
And GKE takes care of all this stuff from there.
You know, like shipping it is very easy for us.
If the matrix looks fine, we can just
flip the time and kind of make it look.
FILLIPO MADELLA: Great.
And you're using PyTorch and deep learning
VMs, which are computer-engineered virtual
machines that are pre-configured with most popular machine
learning frameworks.
So I would love to know a little bit more
how that experience is like for you.
GAURAV AGGARWAL: So it's not that I have
PyTorch as my favorite thing.
So our expert engineer that Google
has been working with us for some time,
he's an expert in Python.
And being a startup, we needed the freedom
to choose how we wanted to do things.
So that's how we are in PyTorch.
We have not seen any kind of complaints.
And people are generally happy.
It's not that it's a [INAUDIBLE]..
We never felt like that.
Everything is pre-installed.
There's no dependencies to be handled.
And there's no issues like that.
Everything they do natively integrates with our pipeline.
Even moving Cloud was very easy for us when we moved from GCP.
So moving the pipeline has been very easy for us, especially
the migration from S3 to Cloud Storage.
I think that was the main concern we had.
But even in that, it was very easy for us.
FILLIPO MADELLA: Thank you, Gaurav.
So to finalize a little bit our conversation here,
there's a lot going on, as we saw.
And so how do you monitor everything that is happening?
And how is your deployment routine?
How do you deploy your applications?
GAURAV AGGARWAL: Our deployment is very much
using Kubernetes pipelines.
We have all of our coding in GitHub.
After every PR push, there's a new label created
and our Docker image is created, which
is pushed back to Kubernetes on our flight experiment.
That runs for some time.
And then it is uploaded to production,
depending on how the area is laid and monitoring kind of
gives us any-- unless there's a bad signal, it takes care of it
and it's being updated.
FILLIPO MADELLA: Thank you, Gaurav.
We just saw how Sleek created an AI-based priority lane
solution so we can all get to our food service,
or any events quicker.
By building the infrastructure on Google Cloud,
they save around $100,000 just last year.
And by scaling their serving platform on GKE
and training their ML models on PyTorch using our deep learning
VMs, thousands of retailers now can boost their profits
while improving their on-site customer experience.
Thanks once more, Gaurav.
And thanks for being with us today and for being a Google
Cloud customer.
GAURAV AGGARWAL: Thanks, Fillipo.
Thanks for having me.
It was great chatting with you.
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