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hey everybody
welcome to solving with aws solutions
your deep dive into architectures built
by aws
and ready to deploy instantly in the
cloud i'm your host sean and joined by
fellow builder
part-time dj mosin mosin's going to be
talking to us about the aws
ml ops framework thanks for having me
sean
awsome lost framework is a solution that
helps machine learning
developers and data scientists
productionize their models
this solution sets up a framework that
takes care of building
deploying testing and monitoring of
machine learning models so that data
scientists can focus on what matters
most to them
and that is building an accurate model
shall we take a look around
let's do it all right most let's dive
into it
how does the solution help customers
take their models from inception all the
way through to production
right so the solution comes with a
architecture diagram
that you can see here and it also comes
with a getting started guide a cloud
formation template
and a link to the source code if you
want to dive in deeper
wow lots going on here let's let's dive
in deeper i see
off the bat there's a few sections in to
this architecture
right how's that working right so the
solution has four sections
uh a provisioning section that you can
see on the left hand side
and three different pipelines
development staging and production
pipelines
so once the the users upload their mod
upload their model artifact into s3
model artifacts
they need to choose which type of
pipeline they want to provision oh so
there's different
types of pipelines that they can
provision that's correct yes
so and currently we have bring your own
model
type of pipeline but uh we soon we will
be adding another another type of
pipeline that will allow customers
to train their models in the pipeline as
well okay so right now they can bring
their own model but soon they'll be able
to train their own model within this
solution that's correct
okay great so how does a user get
started
right so after the user uploads their
model artifacts into this bucket here
they have two options to start the
pipeline one is by calling apis to api
gateway
and the second one is prov by providing
the inputs into
a git repository and committing them
okay so two options how do i decide
which one is the right one for me
right so if you already have an existing
cicd pipeline tool
right you can call that api from your
existing pipeline
by choosing option number one or if you
want to use aws code pipeline right use
option number two which is
providing the inputs to a git repository
that connects to a
aws code pipeline okay so i've got a few
options in terms of getting started
um well once i've got the pipelines
provisioned what happens next
right so once you provision the pipeline
the pipeline consists of three different
stages
okay build deploy and share i see so in
the build stage
aws code build will take the model
artifact from the bucket
and we'll create a docker container and
then we'll store it in amazon ecr
which is a container registry and then
in the deploy section
amazon stage maker will take that
container and then it will create a
model
and endpoint and it will con configure
model monitoring
on it as well okay i see and then in the
share section
alarm the function will take that built
model and it will it will connect it to
api gateway so that the users
can use their model by calling an api i
see
so the build deploy and share steps
those are all within
the very first pipeline that's correct
yes what about the staging
and production pipelines right so this
solution supports multi-account model
that means after the the pipeline in the
development account has done its work
it will push the model into another
account that is staging account
and it will run the pipeline again and
then after that it will push it again
to production environment okay so once
this is up and running
what am i going to see right so once you
deploy the solution
you will see an endpoint to api gateway
and then you need to call the api
gateway just like i'm doing here
and then after you call the api gateway
you will see the response that
stack is created that's that contains
the steps that we just discussed into
the the pipeline so right here after i
call the api
you will see the stack is created here
for me and then this stack contains the
different stages of the pipeline
that does the job for me ah this is
great so essentially this is
helping data scientists with the
infrastructure and the pipelining
required to get their models out to
production
and letting them focus on treating and
retreating their model that's exactly
it well thanks for sharing this with me
yeah thank you
well there you have it a quick and easy
way to bring your ml solutions to life
check out this solution and many more on
the aws solutions website
see you next time
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