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POLONG LIN: Welcome to the Applied ML Summit,
where today we'll be talking about data to deployment
five times faster with Google Cloud.
We've got an exciting presentation today
with a customer, CNA Insurance, who
used Google Cloud to help them accelerate building, deploying,
and scaling ML models in production.
My name is Polong Lin, Developer Advocate for Google Cloud
on Data and AI.
And I'm humbled to be here today with Tintu Pathrose
and Santhosh Ladalla from CNA Insurance, who
will be speaking in a few minutes to tell their story.
But first, just to level set a little bit,
the bridge between data and AI from data to deployment
remains one of the biggest challenges
that enterprises face today in digital transformation.
Without AI, you're not getting the most out of your data.
And without data, you risk stale, out-of-date, and
suboptimal ML models.
So the barriers between data and AI continue to slow innovation.
Data is big, multt-formatted, often siloed, and difficult
to discover or access.
And AI ML systems are often isolated.
So how can Google Cloud help organizations
iterate more quickly and more efficiently from data to AI?
So, at Google Cloud, we've been hard at work
developing a complete environment we call the Data
Cloud.
So Data Cloud is focused on taking those learnings
on operationalizing AI and making them
available to our customers.
The Data Cloud seeks to remove historical workload barriers
between data analysis, model development, insight,
and deployment.
And our goal is to allow you to work with any data
to deliver any innovation in a way that provides
the right tool for the job for the different users that
are involved in the process.
Now, of the various products you saw just now,
I want to highlight two that are especially noteworthy--
BigQuery as the cloud data warehouse for your data,
data analytics, and even integrated ML
and GIS capabilities.
And next, with Vertex AI, from model training
to model registry, model deployment, and ML Ops,
you can build and train models five times faster compared
to traditional notebooks, accelerating speed to market
with model training and development.
And with that, I'll hand it over to Tintu,
who will tell us a little bit more
about how CNA Insurance uses Google Cloud.
Tintu?
TINTU PATHROSE: Thank you, Polong.
Hello, everyone.
First of all, I would like to thank Google
for the opportunity to present at this AML Summit,
I'm Tintu Pathrose, Director of Data
and Analytics Delivery at CNA.
And I have Santhosh Ladalla with me,
who is the Lead Architect of Model Factory.
And today, I want to talk about how
we built Model Factory on GCP in partnership with Google.
But before I get into the details of Model Factory,
let me talk about the business problem
that we had at our hand.
Our team of data scientists use the latest
machine-learning techniques to build predictive models
to solve business problems.
However, the time to deploy these models
and the overall cost was pretty high,
thereby diminishing its value.
We had a vision to build an automated assembly line--
that's called Model Factory--
which covers all key pillars of models and model lifecycle,
including feature engineering, model training, deployment,
and post-deployment.
We evaluated multiple tools in the market
and eventually picked Vertex AI as our choice
to build Model Factory.
The main reason why we picked Vertex AI for Model Factory
was, number one, we really liked the out-of-box capability,
which would help us accelerate the development.
Number two was the ability to build reusable deployment
pipelines using Kubeflow.
Number three was the simplified user experience,
which helped our users.
They didn't have to navigate through different tools.
With this, we entered into a partnership
with Google and Accenture, and we
were able to build the first prototype of Model Factory
within 12 weeks.
And we used this first in VP to deploy 14 models
into production.
The results were very encouraging,
and we were able to deploy these models in 10 weeks.
And it also established a feedback loop,
which enabled our data scientists
to recalibrate these models much more frequently than ever
before.
With this introduction, let me hand it
over to Santhosh Ladalla, who is the architect of Model Factory,
to give you a detailed overview of architecture.
SANTHOSH LADALLA: Thank you, Tintu.
So I'm going to share about the Model Factory reference
architecture that we're working towards.
As Tintu said, we have several challenges
that we want to solve, from our platform,
to development, to deployment and monitoring.
So in the Model Factory reference architecture,
the left side you talk about is our data sources and business
processes, which generate data.
And we ingest that data into BigQuery,
which is our data warehouse-- where
we house raw data as well as the data warehouse structures.
And the highlighted boxes--
the four boxes are the solution components of Model Factory.
The bottom one is model platform, which is Vertex AI.
We use out-of-the-box capabilities of Vertex AI
notebooks.
We also bring in our own container
to enhance via Jupyter RStudio so
that data scientists can build their models with R and Python.
And we have model development, where
we are building analytical data sets from our data warehouse
to make faster the development of the models.
And we are using several services
like Cloud ML and NLP--
so on and so forth.
As part of the model deployment map integration,
we are using Vertex AI out-of-the-box capabilities.
We rebuild Kubeflow pipelines.
And these are reusable pipelines components,
which can be leveraged so that the models can be built faster.
And along with it, after we build and deploy models,
we're also focusing on operationalization
and monitoring these models.
So we are building on top of BigQuery
a scoring repository which will house all the inputs
and outputs of the models so that we can build dashboards.
We're using Looker and HotSpot to build business value
dashboards to monitor these models.
Are the models working?
What is the ROI of these models?
And we are also doing proactive monitoring
using Google Dashboards to monitor our notebooks
and see how they're performing-- how the models are
and what the SLAs look like.
In order to support all this, at the bottom,
you can see shared services.
We are using notebooks.
For orchestration, we are using Composer.
And for CICD, we hire our own concourse pipelines.
And we use Alation for our metadata management.
So, with that, I'm going to hand it over
to Tintu, who's going to talk about the roadmap of Model
Factory.
TINTU PATHROSE: Thank you, Santhosh.
I get so excited when I see that architecture every time.
It reminds me of all the engineering that we did.
Now, let me talk about how we went about implementing
Model Factory.
We took a product-based approach as opposed
to a project-driven approach.
As you know, in a typical project,
there is a start date and an end date.
We wanted to have a framework that
will allow us to continuously enhance
the capabilities of the product, based on user and business
feedback.
As I mentioned earlier, we released our version
1 of Model Factory that included capabilities such as model
training and deployment.
And, in fact, all the new predictive models
that we are developing and deploying
are own Model Factory.
We are now working on version 2 of Model Factory,
where we are incorporating additional capabilities,
including reusable feature layer and model monitoring.
And we expect to continue enhancing
this platform in coming months.
That will enable us to integrate model output more
seamlessly into frontend applications.
Again, I want to thank Google for being a great partner
through our journey.
And with this, I'll hand over to Polong to close.
POLONG LIN: Thank you very much, Tintu and Santhosh,
for sharing how you're building out your model factory at CNA
Insurance using Google Cloud.
And with that, thanks for joining, everyone and hope
you all enjoy the rest of the Applied ML Summit.
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