[Music]
welcome to solving aws solutions your
deep dive into architectures built on
aws
that are ready to deploy instantly in
the cloud i'm your host sean and i'm
joined here by fellow builder and tarot
card reader paul
paul is going to be talking to us about
a solution that helps customers
predict the future in the next couple of
minutes we're going to be talking about
the improving forecast accuracy with
machine learning solution
this solution helps businesses in
diverse domains produce remarkably
accurate forecasts
and compare them with visualization to
suit their business needs
we're also lucky enough to be joined by
a customer app 8
who uses this in the real world let's
just jump into it
all right paul let's just dig into it
tell me a little bit about how the
solution works
well sean our customers are telling us
that the cost of over provisioning or
under provisioning
can really impact their bottom line so
having really really good forecasts
is absolutely critical for their
business i can completely understand
that especially these days
tell me a little bit about the
architecture how did you design the
solution
sure thing well let's hop over to the
improving forecast accuracy with machine
learning landing page now
and take a look at the solution okay
so first things first we want you to be
able to launch this as quickly as
possible so it's available for launch in
the aws console with one click here you
just click this once and it launches
within about five minutes well that's
that's great up and running
pretty fast yeah well let's take a look
at the
the architecture as well so this
solution it really automates the process
of ingesting
your data providing excellent forecast
results
and then visualization for that forecast
i see
so once i've got my data how do i
predict the future with it sure well all
you have to do is you upload it to
amazon s3 and the solution takes care of
the rest for you
so this forecasting section here what
happens as soon as you upload that file
is an aws lambda function triggers it
validates that the configuration file
that you've uploaded
is correct and it will warn you via an
sns notification to your email
if anything was wrong at that point it
triggers the
aws step function which is going to
automate the entire process
of generating your forecast wow so it's
a pretty streamlined solution
yeah absolutely so the forecast is
generated end to end through
data ingestion predictor creation
forecast generation and once it's all
done
you get a an object in s3 which
represents your forecast results
so i'm eager to see the output what does
the future look like
well let's take a look sean so let's uh
let's pretend for a second
that what you've done is you've uploaded
your your configuration file and you're
ready to upload your data sets
right to kick off your forecast so let's
pop over to the amazon
s3 management console here and just to
show you what that looks like
so i'm going to switch over here to uh
browser and file browser
yeah and what i'll do is i'll move my
files over to kick off the forecast
process
immediately and the solution under the
hood as soon as i drop these files in
is going to start automating the
forecast creation for us
so those files are there and if we were
to switch over to the aws step functions
console we would see the step function
running through generating the forecast
right now and if we go over to amazon
forecast we can already see
that a data set group name matching the
file set
file name that we created actually
exists here in amazon forecast
so as these data sets are being uploaded
you can see oh
yep the creation is pending it's already
starting to create your forecast for you
so this is pretty easy you upload your
input data it has your historical values
maybe some additional metadata and out
comes a forecast
absolutely and a lot of our users really
want to visualize those forecast results
as well which is why the solution comes
with a jupiter notebook built into it so
they can get a really good picture of
what that forecast looks like do you
wanna have a look
let's jump in so what we're looking at
here is a jupiter notebook where
developers and data scientists might go
to visualize the output of a forecast
what we're looking at here is a customer
who wanted to demonstrate two different
what-if scenarios
what if i price a burger at 15 or what
if i price a burger at 20
which one's going to sell better so how
do we take a look which is what's the
answer to that question
well let's take a look what the data
tells us here so scrolling down here
what we can see is the two forecasts
that were generated by amazon forecast
the first for that burger at 15 and the
second for that burger at 20
what we're looking at here uh as the
dotted line is essentially the point
forecast
on average this is what we expect in the
future right and i see there's a few
more lines here
yeah you can think of the shaded area as
essentially a confidence interval
so that's an 80 confidence interval in
this in this case
so 80 of the time we're going to expect
the burger to sell
this well i see and so depending on
my own business and i'm how sensitive i
might be to overstocking or a loss of
sales from under stocking
i would probably be more or less
aggressive with the the forecast i
choose
exactly yeah and you can pick whichever
quantile you need to suit your need ah
this is great
i'm surprised how flexible frankly this
is to get up and running a few options
on which prediction i want to use thanks
for walking me through
next we're going to go talk with one of
our customers who is using this in the
wild
we're now joined by hani jabber cto and
co-founder of app8
hani welcome to the show thanks for
having me sean so henny tell me a little
bit about app8 what does it do
oh well app 8 empowers guests at
restaurants to view their bill
and pay it using their phone we also
allow restaurants to
manage their digital menu as well as
offer takeout and dining
orders for guests okay so you guys have
features throughout the dining
experience from browsing the menu
placing the order all the way to
checkout yeah and it's all touchless so
it allows the the restaurant servers to
use their time more efficiently and
better
table turnover and the touchless
solution is especially important now
because guests are a little bit
sensitive to using pin pads for credit
card and debit card payments
oh absolutely and tell me a little bit
how app-8 is
beginning to use predictive analytics
yeah
that's an interesting question so
imagine you're you're
your restaurant you can actually predict
how many people are going to come at a
specific time to your restaurant
as well as how many menu items are going
to be in demand
and what are the menu items that are in
demand this will allow you to better
manage your inventory
you won't have food waste and have a
better
return on your investment honey tell me
a little bit how
op-8 uses this solution to make
predictions
yeah well actually interestingly enough
we started by building it in-house
it was relatively more complicated it
took more time to build
it took time from a data scientist and
it took time from a developer
and also time from development to keep
it running then we found out about the
aws managed solution
it did most of the stuff that we needed
with a fraction of the cost and a
fraction of the time
it was also interesting because we were
able to run what-if scenarios
that are even more important for our
clients
henny thanks so much for being on the
show and sharing your journey with us
you're welcome sean
this solution provides serverless
automation around the amazon forecast
service
it takes you all the way from data
ingestion through to creating a forecast
that's right and it even provides you a
great way to visualize your forecast
output which can save you money
check out this solution and many more on
the aws solutions
you