Welcome to Solving with
AWS Solutions, your deep dive
into reference architectures
built by AWS that are ready to
deploy instantly in the cloud.
I'm Johny and I'm here today
with Mukta from the AWS
Solutions team to tell you
about the AWS ML
Ops Framework solution.
Thanks, Johnny.
MLOps is a systematic process
of taking experimental machine
learning models from training
to deployment
and monitoring them.
Customers often find managing
the lifecycle of machine
learning models to be overly
manual and time consuming.
This solution helps
organizations feel confident in
building MLOps workload by
eliminating the need
of extensive coding.
I'm very interested to hear
more about this,
so let's dive in.
OK Mukta, take me
through the solution.
Sure.
This is a landing page
for the solution.
There is a link for
implementation guide here,
which will give you all the
information about
the solution.
There's a link for
source code, which you can use
to modify according to
your use case.
There's a link for Cloud
Formation template, which you
can use to deploy
the solution.
OK, how about you take me
through the architecture?
Sure.
Let me start with
single account architecture.
There are two components
of it.
One is Orchestrator and one
is Provisioned Pipeline.
The Orchestrator is deployed by
solutions mean template.
The Provisioned Pipeline is
deployed by Orchestrator.
The customer initiates the API
call to Amazon API Gateway.
Orchestrator's Lambda then
creates AWS CodePipeline,
and then AWS CodePipeline,
then provision
the requested pipeline.
Similarly, you can use multi
account template, but you can
have different environments in
different AWS accounts.
OK, and what type of models
does this solution support?
Two models.
One is can pre-trained using
Amazon SageMaker building
portals and industry using
model registry and custom
models for build your
own model scenarios.
And how do I ensure that my
models are relevant and
accurate over time?
You can use solutions,
data quality and model quality
monitoring models,
monitoring pipelines which
which you can use for
continuously monitoring the
quality of
machine learning models.
OK, let's see it in action.
Sure.
So I have a CloudFormation
template deployed here.
You can choose between single
and multi account here, and for
the demo,
I'm using single account.
So once you deploy the single
account template, you can call
the Orchestrator API,
which will then call Lambda to
provision your
AWS CodePipeline.
So I have an example here.
So this is the example where I
am provisioning real time
inference pipeline, which uses
energy boost model to
predict customer churn.
So once you call this API,
it will deploy a CloudFormation
template here, which will then
create your SageMaker inference
endpoint and also
deploy API Gateway.
So once the API Gateway and Sage
Maker Endpoint is deployed,
you can use the Inference API,
which you can get as
an output here.
This is where you get this is
your bring your own model
inference Lambda rest API.
You can use this API to request
the predictions for
your model.
So I have an example here.
So this is this is the example
API call I have.
Once you call this API,
you will get the probability of
churn here, which is 0.46.
Again what are some of the
pipelines that the
solution contains?
Along with real time
inference pipeline, which we
just saw in the demo, we have
batch transform pipeline,
which again uses Amazon Sage
Maker and a custom algorithm.
Along with that, we have
image builder, which uses
Docker images and then model
quality to continuously monitor
the deployed
machine learning models.
Awesome.
Thanks, Mukta.
Thank you.
Thank you for joining our
review of the AWS
MLOps Framework,
which simplifies the process of
taking an experimental ML model
into a production system.
Check out the solution and many
of those on the
AWS Solutions webpage.
Thank you for watching.