Good morning and welcome to This Is My Architecture.
I am Dario. Today I am here with Luca from Neosperience.
-Good morning, Dario. -What do you do at Neosperience?
Neosperience is a software company
whose goal is to inspire a feeling of empathy for technology
giving to brands and marketers the opportunity to understand
what makes their customers special and then use targeted messages
- one by one. -That is interesting.
So, can you show us some of the architecture today?
Yes, let's see the Image Memorability function from Neosperience Cloud.
Memorability? What is that?
It is a group of deep learning models
that estimate how much an image remains impressed in the memory
of those who look at it and then define
what features of that image make it more memorable.
That is useful for marketers because it is a valuable instrument
for the creation of marketing campaigns.
Interesting. How does it work?
The image is uploaded from the marketer's PC
to an S3 bucket
that activates a Lambda function through a trigger.
The Lambda function performs some tests on the image
if it fits, if the format is supported and the size is suitable
and then creates a task to be sent to a SQS queue.
In the SQS queue there is a Lambda function
which scales to process this specific task
using deep learning AMI applications in the cloud
or GPU On-Prem.
So, this is a hybrid architecture.
Yes, because processing a deep learning resource for memorability
requires the use of GPU from the inference.
Hence, the Lambda function
uses a DynamoDB table to verify
the availability of valuable records to process this task.
In case of a negative response,
Lambda activates the appropriate deep learning AMI instance.
-Got it. -The deep learning instance
pulls out from the SQS queue the job, the information
and the necessary data for the processing.
Once the inference is over, the image of the heatmap,
concerning those features of the image
that are more memorable,
is sent to the client along with notifications through SQS
to the marketer's PC
and to third parties services
about the results on the memorabiity.
-And this is the end of the process. -Of course.
It is very interesting. So, we are talking about a serverless architecture.
-Absolutely. -What is the core of it?
The core of the architecture is the set of the Lambda functions
that scale the deep learning and the On-Prem instances
based especially on the image features
and on the model chosen.
Very interesting. Are you already thinking about future developments?
Yes, absolutely.
We are planning to use new functions of API Gateway,
like WebSocket support,
to entirely separate the client from the services part,
allowing the communication
completely rest and WebSocket through endpoint
-for the push information. -Ah, I get it.
We have another development in mind, thanks to the release
of the Lambda Layers and the Elastic GPU support at the last reinvent.
Once the Elastic GPU are available and supported on the Lambdas
our goal is to remove
the use of a deep learning AMI instance
to carry out the entire process with serverless Lambda functions.
That would simplify the architecture.
Granting more scalability to the system
which does not have on/off latency of the AMIs.
That is extremely interesting.
-Thank you very much, Luca. -Thank you.
Thank you all for watching this and for being with us
during this session of This Is My Architecture.