Hello and welcome to 'This is My Architecture'.
I'm Don with AWS and I'm joined today by Nagaraj with ITTStar.
Hey Don, thanks for having me here.
You're welcome. Tell us a little bit about ITTStar.
ITTStar delivers global solutions, global software solutions
with emphasis on digital transformation using data and analytics.
So why don't you tell us about the architecture that we're going to go through today.
So this is the architecture that we built for--
This is a solution that we built for a luxury watch client of ours.
They are in the business of brand and pricing guidance for wholesalers and retailers.
Okay, excellent.
So this is a way that we can gather information,
come up with pricing in a market that doesn't follow traditional supply and demand.
I see that you have Amplify.
Why don't we start from the left and go through the flow from a user's perspective.
Sure. So we used Amplify
so that the customers, the retailers and wholesalers, use mobile and web applications to log in.
So for that we build the UI framework using Amplify.
Here we have to authenticate.
Not everybody can come in and browse the luxury codes.
So for that we use API Gateway and Cognito for authentication.
Okay, can you show us the flow? There you go.
So users coming in to Amplify, perfect.
So API Gateway being serverless.
Serverless, absolutely, and uses Cognito to authenticate the users.
And then, as far as the flow, so am I uploading data?
What am I doing in this particular use case?
Once authenticated, the users can do two things.
They can either upload their inventory details,
or they can post their inventory to the third party retail sites.
So, once they upload, the API Gateway again kicks in
and the images are stored in S3.
So these are in the raw bucket.
As soon as the image reaches S3, a Lambda is triggered.
Okay, now what does that Lambda do?
It's being triggered by a data event, what's the next step?
So the Lambda takes the images or video that is hosted on S3
and invokes Rekognition.
Okay, so are you using custom labels with Rekognition?
What have you done here to train it? What data sets have you fed?
Can you give us an idea of the size?
Absolutely. We used around 100,000 plus images
of various luxury goods to train Rekognition
so it can identify various features,
brands, and even if they post accessories we will be able to identify accurately.
Okay, so this is a model that is extensible onto lots of different markets.
You might start with one luxury good and branch off into others.
Absolutely.
And we should also remember that every item has multiple features.
So able to clearly extract those features is one of the challenges we had
and Rekognition helped us solve it.
So I see Rekognition, we have Textract,
I'm assuming we're cropping images coming out, what's happening next?
How does Textract fit in?
All the images are not like, plain and simple.
They come with a lot of text whether on the image or beside.
So the way we used Textract was to extract those elements
and correlate it with the image.
Excellent. So that sounds something novel.
Is that something unique to what you've done?
Absolutely. So we had to use many--
There's a lot of logic that went behind this.
First and foremost, we had to identify the geolocation of the text within the image.
And then able to correlate it with the image.
And more importantly, identify what does that text mean.
It can be as simple as a brand name or a model number
or any specific feature.
So it sounds like that data, ultimately that metadata, is going to land in Dynamo.
And it sounds like maybe there's an opportunity in the future for Comprehend.
That's correct.
With Comprehend's capability of extracting entities and keywords with ease,
we are definitely looking at Comprehend in the future.
But as far as, once the data is extracted, all the text goes to DynamoDB
in the form of JSON format
and we made it extensible for the feature level or the database level.
That sounds like a very good design choice.
So now in the future, if you decide to have additional features you want to extract,
or as you get into a luxury market, a goods market, that has lots of additional metadata or features,
you're able to extend that with Dynamo.
Absolutely.
Thanks for sharing your architecture today.
This is a very innovative use of both serverless and AI services like Rekognition.
- I appreciate your time. - Thanks for having me.
And thanks for watching.