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JAMES MA: Hi, everyone.
Welcome to our session at Google Cloud Next,
exploring how Vodafone, one of the world's largest telecoms
companies, is supercharging AI and machine learning
at scale using Google Cloud, transforming
how their business operates, and ultimately transforming
the end customer experience.
I'm James Ma.
I lead the technical account management team
in EMEA in the telco industry.
I'm delighted today to be joined by Sebastian from Vodafone.
Sebastian, can you tell us a little bit about your role
at Vodafone?
SEBASTIAN MATHALIKUNNEL: Yeah, sure, James.
So very happy to be here.
Hi, everybody.
I'm Sebastian.
I lead the machine learning strategy and roadmap
on Google Cloud for Vodafone.
JAMES MA: Brilliant.
Thanks, Sebastian.
So today we're going to be talking
a little bit about the data science challenges
that Vodafone faced before embarking on this program, so
rewinding 18 months to two years ago, and the solution
to that, which has been AI Booster, which is their machine
learning platform built on Google Cloud.
We'll talk about what that is, some of the benefits
that Vodafone is seeing, and some of the business
challenges they're solving with AI Booster.
And then we'll dive under the hood
and talk about the technology and the architecture
that sits underneath the platform,
and then ultimately how Vodafone has
been able to adopt the platform and change
the business successfully.
Before we start, though, I think it's worth
touching on the broader Vodafone and Google Cloud partnership.
We've been partners for a number of years now.
And Google Cloud has been helping Vodafone, particularly
with their Tech 25 strategy, shifting
from being a telco to a tech comms company,
particularly transforming data analytics at scale
and unlocking the potential of data,
leveraging AI machine learning, which is some
of what we'll talk about today.
OK, so let's first of all start talking about the data
science challenges, what it was like before AI Booster, rewind
two years ago.
So, Sebastian, before AI Booster,
in Vodafone Commercial, you're pretty advanced
already in the data science you're
doing, with more than 200 data scientists
operating across your local markets
in the UK, Germany, Italy, Spain, so on,
with thousands of models--
machine learning models.
What did that look like, and what
compelled you to go on a journey like AI Booster?
SEBASTIAN MATHALIKUNNEL: Yes, sure, James.
So like you said, Vodafone is pretty mature in its data
science journey.
But looking back two years, it was actually this exact problem
of size and scale of Vodafone data science operations
that led us to believe that we could
have a problem on our hands.
So, for example, it used to take us
lots of steps to spin up a production GCP environment.
And this is simply because it is a complex operation.
It does require multiple teams such as engineering,
architecture, security, local markets to make this happen.
At that time, a lot of these handshakes were manual as well.
So what this meant was that it was really difficult
and took a long time to create a GCP environment.
And secondly, each environment, because
of the manual handshakes, used to look slightly different
from each other.
So we thought this is the first problem that
was worth tackling.
But also slightly related to this problem
and emanating from the fact that every environment is ever
so slightly different, well, if you
had a use case that was developed
in one of these environments and you
wanted to scale it across markets,
it used to take a lot of time and effort
to replicate the same thing across markets,
because each environment was different.
So we called this a horizontal scaling problem.
And slightly complementary to that was the vertical scaling
problem, which is simply, how do we
take a data science notebook or data science process from POC
to production in the quickest possible manner.
So in a nutshell, these are the three kind of broad issues
that we are hoping to tackle.
JAMES MA: OK.
Absolutely.
So you were looking for more standardization, reuse, speed,
security, and, of course, the ability to innovate.
So that led to the solution, which was AI Booster.
And in a nutshell, AI Booster is Vodafone's scalable, unified
machine learning platform.
It's built entirely on Google Cloud, Google Cloud Managed
Services, in fact, which we'll talk about in a moment.
It's got in-built automation, scalability, and security.
In terms of the benefits that AI Booster brings, for me
a key one is the performance.
So that time to deploy models, we've
pulled that down significantly.
You said it used to take four to five months,
or could take four to five months,
to deploy, to go from POC to production.
And we brought that down to a matter of weeks,
which is a huge improvement.
Sebastian, I guess, what would be the key benefit
that you would want to call out from AI Booster
that you're seeing?
SEBASTIAN MATHALIKUNNEL: Yes, James,
so in addition to the performance aspect
that you mentioned, I think the cost
aspect is interesting as well.
So I think the obvious thing is you're
going from a lot of manual processes and multiple teams
to completely automated environment.
So the cost aspect is pretty obvious there.
But also I think what's interesting is
that we are going to provide unlimited instances of AI
Booster to data scientists.
But if you think about it, that's
only going to be financially viable if the cost does not
scale linearly with the number of instances
or with the amount of infrastructure that is spun up
within these instances.
And I'm happy to say that today that's exactly the situation
that we have managed to achieve.
So the cost of AI Booster is almost 100%
traceable to the exact compute that was done
against that infrastructure.
JAMES MA: That's really good to hear.
So I think there are some obvious key benefits
to Vodafone of using AI Booster--
the performance, cost, and being up to scale
across the enterprise, the horizontal and vertical scaling
you talked about.
But ultimately you're doing this to improve your end customer
experience and solve some of the business challenges.
Can you talk about one or two of the key use cases
that you've now got running on AI Booster?
SEBASTIAN MATHALIKUNNEL: Sure, James.
I would like to start with Net Promoter Score, or NPS, simply
because that was the first use case
that data scientists trusted us to take on board into AI
Booster.
And what we're trying to do with NPS
is trying to get to know or measure
the happiness of our customers with our products.
Where are we doing a good job?
Where can we improve?
So as you can imagine, it's a pretty important use case
for us to have.
JAMES MA: Yeah, brilliant.
And recommenders and loyalty platforms--
I think people are quite familiar now
with recommendation systems.
But of course, the machine learning
models that sit behind those are extremely complex.
That's now on AI Booster?
SEBASTIAN MATHALIKUNNEL: Exactly, and like you say,
recommendation models can come in all shapes and sizes.
And the team working behind this have done a great job
in translating all of that complexity into AI Booster.
And so that also gave us a shot of confidence
that, yes, this can be done.
JAMES MA: OK, brilliant, so we've
talked about when you brought to life
AI Booster in terms of how you're using it.
Let's spend a bit of time now diving under the hood,
looking at the technology and the components
and the pipelines that make up AI Booster.
Now, it's a quite complex-looking diagram.
We're not going to go through all of this.
But what are the key things you're seeing--
what are the key things we see when we spin up an AI Booster
instance?
SEBASTIAN MATHALIKUNNEL: There are two key takeaways
from this diagram, I think.
The first is, we are talking about AI Booster instance.
Well, what does it actually mean?
As you can see from this diagram,
it's just a bunch of GCP projects
that are wired together and automated
in a pretty open-ended way.
So to be more specific, you can see,
we have different environments, such as lab, non-live,
and live.
And within lab, for example, that
is the only environment where there
is a Jupyter-based environment.
So that's where almost 100% of the POC work happens.
And by the time we reach the live environment,
almost everything has to be automated,
because there is no access for any human agent there.
It's just completely automated.
So all the processes need to be automated by the time we
reach the live environment.
JAMES MA: OK, brilliant.
And some of the components here, then--
the key components, as we said, are the Google Cloud technology
that sits behind them.
Obviously there's Vertex AI.
That's our unified AI platform where
you build, deploy, and scale machine learning models.
We'll talk about Vertex training,
Vertex pipelines in a moment.
But as you said, one of the really interesting parts
of AI Booster is the automation and the CI/CD
that you've been able to achieve with the platform.
So can you tell us a bit about what you use for that,
and how is it compared to what you had before?
SEBASTIAN MATHALIKUNNEL: Absolutely.
So Cloud Build was a tool that we heavily
utilized within AI Booster.
And the reason for that was we are
going towards an environment which is almost 100%
managed service.
So there is no virtual machine, compute engine,
or manual clusters inside.
100% of that infrastructure is built and based out
of lightweight containers.
And previously we had Jenkins, which
is also a virtual machine-based infrastructure.
And so it was just a natural choice for us
to move to Cloud Build, which was 100% cloud
native and container native.
But in addition to that, what I'd say
is it's also interesting to see how
Cloud will impact with other tooling, such as Artifact
Registry.
So for example, security was a process
that was completely manual.
So if data scientists wanted a particular Python
package or a Linux build package,
previously that used to trigger a manual process which
used to go to security teams to be vetted out.
And then once it's vetted out with security,
we needed to bake those images into the VM
for it to be available, whereas now with the combination
of Cloud Build and Artifact Registry,
that entire cycle has been automated.
What that means for data scientists
is that, from the moment they demand a new dependency
to the time that they can use it,
is almost done in real time.
JAMES MA: OK, brilliant.
So we've looked at the architecture, the lab,
non-live and live, and the wiring between that,
some of the key components.
Now let's talk about how that all
fits together, the flow, the end-to-end pipeline,
and, of course, this is all automated.
What are the key things you'd like
to highlight here, Sebastian?
SEBASTIAN MATHALIKUNNEL: Yes, James,
so we're going from a custom coding-based approach
to machine learning engineering to everything working based
on standard components and pipelines that
tie those components.
And as a result of that, there's a few interesting patterns
that we're seeing, the first of which is data contract.
So previously, when we used to analyze any machine learning
workload, we used to find that about 30% to 35% of that code
was simply related to data quality, data validation,
or cleansing, whereas now what we're able to do
is completely automate that process.
So what we do is, the first time that the data is ingested,
that triggers an analysis of the data
in terms of its distribution and different characteristics,
which then forms a contract.
What we then do is get broad agreement
against this contract with our various stakeholders,
such as data scientists or data owners.
And once we have that agreement, we stick that contract back
into the pipeline.
And what it's able to do is, every time it then runs,
it's able to automatically validate
that the data meets the requirement that it
was signed off against.
JAMES MA: OK, so you check and validate the data.
And then you call the Vertex training service.
SEBASTIAN MATHALIKUNNEL: Absolutely.
I mean, this is another area where
it used to be very painful for us to manually have
to manage big data or machine learning clusters,
whereas now with Vertex training, all of that
is completely automated for us, and in most cases
is just a single click affair.
So that's definitely made our life much easier.
JAMES MA: OK, so in terms of the pipeline,
you check validated the data, you
do the training on the data, and then, of course, you
want to know the performance of your models.
You'll want to choose the best performing model.
And how you do that in an automated way,
through Champion Challenger, is pretty interesting in terms
of the pipeline.
And coming back to the point around standardization
and automation, that's all in-built into the pipeline.
So we've talked about the technology
now, which, of course, is a really key component.
But it's just one component of the broader change journey.
And adopting AI Booster, of course,
is also the processes and the people.
And perhaps one of the most interesting and important parts
for me has been the people side of things,
looking at the new roles and the training
that we've done, a new role being
the machine learning engineer.
How has that been introduced, or how does it
compare to some of the other roles that have existed today,
Sebastian?
SEBASTIAN MATHALIKUNNEL: Yes, James, so like you mentioned,
the people aspect of technology is
something we consider as important as the technology
itself.
And in this respect, I must thank
Google for the incredible support
that you guys have provided us right through the journey,
including the very custom ASL program that we crafted
together in order to create a new function within Vodafone
called machine learning engineering.
And this has been very well received
by all of our stakeholders.
So I just wanted to highlight that as well.
JAMES MA: Brilliant.
Thank you.
I know the team will be very proud to hear that the ASL--
the five-week training was really intensive,
but a really good--
almost a game changer in adopting
AI Booster in Vodafone.
And finally, of course, it's been
a long journey, although we're still near the start of it.
We've got the platform live in production, where
we've onboarded use cases.
But there's so much more potential
we can achieve with the platform.
Getting here has been no mean feat.
We've had input and support from Googlers all around the world.
We've had teams in Vodafone, technology and commercial,
and of course, our brilliant partner datatonic,
who've bought machine learning expertise to this program,
has been absolutely fantastic.
So thank you to everyone that's been involved.
SEBASTIAN MATHALIKUNNEL: Absolutely, James.
So, like I say, this is still day one of AI Booster.
But just within the few months that we have gone live,
we already have over 100 instances of AI Booster
across the different local markets.
So I just wanted to thank, on behalf of myself
and my stakeholders, all the teams responsible for making
this such a big success.
JAMES MA: And thank you, Sebastian,
for sharing your story.
And thank you for listening, and I hope
you enjoy the rest of Next.
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