Hello, and welcome to this new episode of 'This is My Architecture'.
I am Sebastien, and I'm joined today by Mohammed Sleeq from Aramex.
-Hello, Mohammed. -Hi, Sebastien. Thank you. Thank you for having me.
So, can you tell us a little bit more about Aramex, please?
Sure, Aramex is a comprehensive logistics and transportation solutions provider.
We operate in 65 countries, and we have 15,000 employees globally.
The company moves products, everything from heavy industrial freight
into international cross-border ecommerce and domestic fulfillment as well.
And we have over 250 distribution centers across the world.
Okay, and can you tell us a bit more on the challenges you are going through and how you're planning to solve them?
Of course, so one of the main challenges that the industry is facing today,
specifically in our emerging markets,
is the lack of a proper address management structure
within our regions as compared to some of the mature markets
where there's reliance on postal codes and zip codes
for the drivers to navigate their addresses and to navigate their deliveries.
So, for us, there's a lot of dependency on descriptive addresses.
So, we really wanted to deploy a generative machine learning algorithm or a text-matching algorithm
that looks at an address in reference to historical data and come up with an XY coordinate,
to reduce dependency on a skilled worker that looks at an address and tries to sort it
and to reduce the number of calls that we make for customers to acquire addresses
and to improve customer experience, at the end of the day.
Okay, so can you walk us through this architecture, please?
Sure, so the solution has two main flows.
The first flow is really about advanced analytics
and machine learning capabilities to build our machine learning models.
And the second one is really about empowering our data scientists for the end user flow.
So, the first element, we use Amazon Glue
to move large amounts of data from our data sources as an ingestion service into Redshift.
And we store three months of data in Redshift for the data scientists to build their models.
But also, we use S3 to ingest large, large amounts of datasets,
and in this scenario, a lot of address data into S3, which is really a scalable, efficient data lake,
a centralized data lake, where we ingest all of this data using Amazon Glue.
And we also have another stream where we move reference data
for our data scientists to be able to run rapid, some references on rapid access to the data,
run their tests to make sure that they optimize their machine learning models
and build their predictive models.
Okay, so you mentioned, so you're only keeping three months of data on Redshift.
So, can you explain us a little bit more why and any specific processing you're doing on it?
Sure, so it really helps our data scientists to see months of data for data exploration and data preparation,
and also some of the data transformation that we needed to do to transform it from raw data
into data that is used for accurate prediction to build accurate prediction models.
So, anything from profiling, from data cleansing,
and data transformation to be able to build proper machine learning models.
And then the next step really, is for us to build an optimized model using SageMaker.
So, the building of the model, it fetches data.
So, SageMaker fetches data from Redshift to build an operating model and optimize them.
In order for us to train these models to become a bit more accurate,
we use Redshift Spectrum to fetch the data from S3, to make these models a bit more accurate.
So, the minute the precision of these models are accurate enough to drive business outcomes,
we are able to deploy these models onto our operating systems and driver apps
and all the front-end applications to consume SageMaker and deployments
using the serverless approach through the Amazon API Gateway
and Amazon Lambda to consume these models from Amazon SageMaker
that is predicting accurate addresses and accurate XY coordinates.
And this happens in a matter of seconds,
so we can deploy and we can add value to the business on the cloud.
Okay, so I understand that SageMaker is one of the key components you have in your architecture.
So, any specific reason why you're using SageMaker?
Of course, so data scientists need to use tools, very advanced tools,
to deploy their models and to drive massive outcomes very quick and very fast.
And typically, we were looking for a machine learning platform
that has all of these components in a managed-services manner.
I think SageMaker offers the capabilities to build, to develop,
and to train or even deploy our models all on the one platform,
so that we can maximize the value very quick and be able to add value to the business as quick as possible,
and value-to-market becomes very relevant in this scenario.
Okay. And then so, what was the main benefits of besides, so all the capabilities from SageMaker?
So, any any business benefits out of this architecture?
So, imagine when the shipping start for fulfilment journey very early on
when we create the shipment in our landscape using Amazon SageMaker
and using these training models and API models,
we are able to geocode our addresses very early on in the fulfilment journey of the shipment,
so we can optimize our last mile capacity and optimize our routes.
And also, we also use it to plant capacity very early on in the journey.
So, we've seen a 10% improvement on our average success rate
on daily basis in our emerging markets and core markets,
because we're improving the quality of the addresses,
and we're using advanced tools to navigate and empower the driver
to navigate even better and optimize the routes.
And this is huge for us.
So, we improved the productivity of the driver by almost 10% in these core markets.
Okay, that's great. So, thanks for walking us through your architecture.
Thank you so much. Thank you for having me.
And thank you for joining 'This is My Architecture'.