BRUNO AZIZA: Hi, everyone.
I'm Bruno Aziza.
And today, I'm sitting down with Polong.
Now, you know Polong because you might have picked up
on the content he's built for product recommendation design
patterns.
What are design patterns?
They are solutions that help you assemble technology
to solve a business problem.
And Polong is at it again, because this week,
we're releasing a new design pattern for demand forecasting.
So we're going to talk about what that is,
how you can use it precisely.
Polong, thank you so much for giving us
some time this morning, in fact, this afternoon, because you
live in London, where you guys just
had a snow storm, gigantic snow storm.
And I think you know how to deal with that because you're
originally from Toronto, right?
POLONG LIN: Yeah, that's right.
It's kind of quite cold here in London.
And it's reminiscent of my time back
where I grew up in Toronto.
So it's kind of funny to see the car struggling a little bit
here in London-- kind of reminds me of how rare it
is to see snow fall in London.
BRUNO AZIZA: Well, hopefully, you're staying safe at home.
And thanks for the work that you're doing for these design
patterns.
Let's get into it.
Tell us a little bit more about why you
decided to pick this pattern.
POLONG LIN: Absolutely.
So this pattern is all around demand forecasting.
And so whether you're a consumer or business today,
and no matter where you are in the world,
you could go to a store, let's say, hoping to make brownies,
and then realize that they're out of chocolate powder, cocoa
powder.
And then, meanwhile, you look in the grocery aisle,
and you see sacks of potatoes.
And they're starting to turn green.
They're starting to turn bad.
And this is kind of like the essence of demand forecasting,
is when businesses can try to anticipate or sometimes not do
well in anticipating the demands of the consumers.
So in the case of chocolate powder, maybe
there wasn't enough chocolate powder.
They didn't quite anticipate the demand
during this particular season or particular day of the year.
Whereas for potatoes, they had a surplus.
And so they had too much of something.
And so demand forecasting is all about
trying to anticipate the right amount of, let's say,
product or item that you might need,
or resources that you might need.
So you really try to save money on things
that you don't need to overly supply, while at the same time
dedicating more of those resources towards things
that you can anticipate will have much higher demand,
essentially.
BRUNO AZIZA: Typical demand-and-supply problem.
And I'm sure people are very familiar with this problem
in retail.
But this is also appropriate for other industries, right?
POLONG LIN: Yeah, absolutely.
There is, for example, energy.
Energy providers have to try to predict how much energy they
need to prepare on an hourly basis, I assume.
And there's obviously daily cycles.
So during the day versus during the evening,
people use energy differently, their electricity
at home differently when they're cooking
versus when they're outside, or, I guess, nowadays, when most
people are at home as well.
And then there's also seasonal cycles,
because when it gets colder, like it is now here in London,
people may tend to use a little bit more energy.
So energy providers have to really think about that.
And it's not just about energy that could be mobile apps
or telecom or online gaming services,
where if you have a lot of users,
and they suddenly come to your service
because the holidays or a weekend or something,
and you don't have the right resources allocated in place,
that could disrupt the service for everyone.
So it's not just not being able to make brownies.
It's, no one can make brownies.
No one can enjoy brownies.
Or essentially, translating it back to that but your domain,
no one can enjoy the service because, let's say, the servers
are down.
And so for that reason, demand forecasting
can really span across lots of industries
and be really impactful to the end consumer as well.
BRUNO AZIZA: So very relevant to retail, of course,
energy, telco.
But this problem of demand and supply
is present across the entire economy.
And that's why, with this solution,
we know we're going to help many, many customers.
In fact, a lot of these design patterns-- they
originate from customer demand.
So tell us a little bit about how
this is going to impact the customer deployment
and how you've seen this in action.
POLONG LIN: Yeah, absolutely.
So you can do demand forecasting today
in a variety of different ways.
You could use different kinds of technologies and softwares
and programming languages.
This demand forecasting pattern focuses
on using, training, deploying demand forecasting models
with BigQuery ML.
And so if you think about, to kind of take a step back
and think about how it machine learning models typically
get used in production, traditionally, a business
may have their data in, let's say, a data warehouse.
And they often end up extracting that data out of the data
warehouse to train and deploy the machine learning models.
But this can run into some difficulties.
So if you're extracting data from a data warehouse, number
one, that's a data governance issue sometimes.
If you have any sort of PII data coming out of a data warehouse,
you now have to track where all your data is going and worrying
about data leakage.
That's one thing.
And then the second thing is around
provisioning the right resources to train your machine learning
model and finding a place to host your machine learning
model as well, trying to deploy your machine learning model.
And these are all complexities that can really bog down
a machine learning pipeline.
It can make things quite a bit longer or more complex
for you to go from zero to a fully productionized machine
learning pipeline very quickly.
And that's where BigQuery ML really fills in this gap.
So BigQuery, which is a fully managed, serverless data
warehouse-- you know, you have your data storage,
let's say, all your transactional data directly
in BigQuery.
You could do your analytics also in BigQuery,
and it's very easy to do so.
And now with BigQuery ML, you can
do all your machine learning workloads,
train and deploy your machine learning models directly
with BigQuery ML.
So you have this kind of all-in-one sort of thing.
By that nature, it means you don't
have to worry about data governance,
and you don't have to worry about finding
some extra infrastructure, exporting, finding
some other service to be able to train and deploy your models.
So that's where BigQuery ML fits into demand forecasting--
making things a lot easier for customers who go from months
to creating a machine learning pipeline down to,
let's say, weeks, or weeks down to days.
So an order of magnitude faster by using BigQuery ML.
BRUNO AZIZA: So you have the benefits of the governance
issues you don't have to worry about,
and the time to market, if you will,
because data doesn't have to move.
It's integrated into the same suite.
That's a great overview.
Tell us-- if I'm listening to you,
and I want to start using this pattern,
what do I do next here?
POLONG LIN: Absolutely.
So you can explore the pattern, and Bruno will point
to this outside of the video.
But the pattern will essentially give you a bit of a quick start
into figuring how to do this in your own environments
very quickly.
So starting off with the sample retail data set
that you can replace with your own data set--
so we have this sample to start with,
and then essentially showing you how
to train that model with BigQuery ML.
This is a time series demand forecasting model
that you'd be using with BigQuery ML.
It's very easy, just not too many lines of code.
And you have a model.
And then number 3, then how to make your forecasts
once the model is trained within BigQuery ML.
And again, this is all within BigQuery.
You don't have to exit the ecosystem at all.
And then finally, once you have your models,
you have your forecast maybe running
on a daily, weekly basis, you also
want to make sure your models are updated and staying fresh
so that with new data coming in, you're constantly
updating the models that you really
can forecast and anticipate demand based off
of recent data.
So I show you how to do that in the pattern as well.
BRUNO AZIZA: That's fantastic.
Well, thank you so much, Polong, for taking us through it today.
Thank you so much for the hard work you've
put into these design patterns.
If you're listening to us, and you're interested,
make sure you click on the link down below to get started.
And of course, I hope that you can reach out to Polong
as well if you have specific issues at your account level
and you want help.
We're here to help.
That's what these resources are for.
Polong, thank you so much for the time today.
I'll talk to you soon.
POLONG LIN: Thank you.
Take care.