(mellow music)
- I'm Alex Kruger.
I'm a data scientist and
machine learning engineer
at Sportsbet.
So Sportsbet it's an
Australian online bookmaker.
We have around 700
employees across Australia
and we serve about 1.2 million
unique customers every year.
This might sound a bit
cliche but data underlies
every decision made at Sportsbet.
And I don't just mean
the high level strategic
type of decisions, I mean,
everyday realtime decisions,
like what content to
display on a homepage,
or what are the odds of a
particular team winning?
We are trying to use personalization
to drive customer loyalty and
increase the switching costs
from them going to a competitor
by basically using AI to
personalize their experience.
We've tried to personalize
the homepage to the customer
by giving them relevant events
that we think they will bet on,
or they might be interested in.
And that ultimately
increases their engagement,
high click-through rates and
higher customer retention.
Prior to Databricks, the team were
mostly using their own desktop PCs
to do all of the model development.
And that comes with a
lot of obvious challenges
like getting the data
into the local machine.
We'll either have to be
all of the ETL on Redshift
or downloading a raw format from S3,
neither of which are ideal in
terms of cost and efficiency.
Other challenges also, once the data
is in the local machine,
to be able to process it
all in memory you either have
to subsample it or stream it
and it's a lot of effort to go through.
It would take like months of effort
of going through the exploration
to operationalize that
model in production with,
yeah, that will be months of effort.
At Sportsbet we mostly use
AWS for all of our processing
and services and to data capabilities.
And Databricks very naturally
integrates with all of that.
Using Databricks has been just increasing
the amount of compute that we can spin up
at any given moment.
It reduced, it removed
a lot of the constraints
we had around computation
when we were using
our local machines.
Now we're able to spin up
clusters as large as we want
and process as much data as we want.
In terms of business value
one of the big things for us
has been the faster time to production.
So we're able to take these cases
and turn them into
operational data products
a lot faster than before.
And that also means that by
operating these use cases
means that we can get
feedback from our customers
a lot faster in the form of data.
And it basically shortens
the whole feedback cycle
and allows us to literally
really improve our models
much faster.
And then ultimately increases
the customer retention
and improves the customer loyalty.
In terms of the technical value,
one of the big things for us
has been the data gathering
and pre-processing step.
It's about five times
faster than before now.
Another one is managing environments
and configurations of infrastructure.
That is basically, time spent
on that is basically zero now.
Whereas previously we had a cloud engineer
helping us to do that.
So that was a lot of effort
and time gone into that.
And in general, we've
been able to increase
the computing capacity like tenfold
because previously we was
stuck with the amount of memory
that we had on a single desktop.
Now we can spin up clusters
as large as we want,
so we're able to process
any amounts of data
in a reasonable amount of time.
Ultimately data bricks
allows us to take use cases
and move them into operational
data products a lot faster.
And it also takes out
a lot of the guesswork
and overheads of the whole
model development process,
allowing the data scientists
to focus more time
on doing the actual data science.
And that gives our customers
a better user experience
which ultimately drives a higher retention
and increased revenue.
(mellow music)
