How To Create Intelligent Digital Twins Using Xmpro Ai
Welcome to our comprehensive webinar hosted by Gavin Green, our VP of Strategic Solutions, titled "How to Create Intelligent Digital Twins Using XMPro AI." We invite you to join us on this enlightening journey, as we unveil the transformative power of Intelligent Digital Twins in today's rapidly evolving digital landscape.
In this webinar, we dive deep into XMPro's groundbreaking AI capabilities that are revolutionizing how businesses and organizations operate. You'll learn about the unique features and benefits of XMPro's AI solution, including the utilization of Embedded AI, Augmented AI, and Innovation AI - three foundational pillars that supercharge the effectiveness of our Intelligent Digital Twins.
This is more than just a lecture; we have prepared a live demonstration where you can witness the full scope and power of these Intelligent Digital Twins in action. See for yourself how this cutting-edge technology can be leveraged to empower organizations, driving greater efficiencies, and fueling innovation.
Beyond this, there will also be an interactive Q&A segment where you'll get the opportunity to engage directly with our team of experts. We welcome your curiosity, your queries, and your viewpoints as we explore this exciting frontier together.
No matter if you're a seasoned professional in the field or a newcomer intrigued by the possibilities of AI and digital twins, this webinar will provide you with valuable insights and knowledge that can transform the way you view and utilize technology.
Don't miss out on this opportunity to elevate your understanding of Intelligent Digital Twins using XMPro AI. Join us and become a part of the digital revolution!
🔔 Subscribe and hit the notification bell so you won't miss any of our future videos.
#XMProAI #DigitalTwins #AIWebinar
Transcript
hello everybody and welcome to our XM
Pro AI
um for intelligent digital twins webinar
my name is Kevin green I look after
strategic solutions for XM Pro I want to
thank you for your time attending today
if you've got any questions please send
them through I'm not trying to answer
them at the at the end
in some prior webinars we went through
the four pillars of the the excellent
product I'm not going to go into detail
here
but just in a continuation of that which
pieces are we going to be focusing on
this is in line with our i3c framework
and it's in essence where we focus our
effort when we're putting the product to
Market and the different feature sets
that we are are working on
today's Focus however is going to be on
the AI side of things and what is in the
product that can help you for the
intelligent digital twins with the the
focus on the AI site
before we jump into you'll hear me
talking around intelligent digital Twins
and digital Twins and there is a
difference between the two of those
there was a paper that was written on
the evolution of digital twins
um the two fathers of digital twins
Being John Vickers and the second being
Dr Michael Greaves
um this is Dr Michael greve's vision of
the different stages of evolution of
digital twins these are some slides from
the paper the paper is available down
the bottom and if you're interested we
can send you the link where you can
access the paper as well and it outlines
the different stages and it's also used
as inspiration and a guide within our
software and what it is that we do the
the main Evolution steps you'll see them
on the right there going from zero being
traditional 2D
that evolved into transitional so you're
number one which was 3D cat that went
into number two around the conceptual
this is where
um things started becoming a lot more
model based and then that evolved into
the concept of a digital twin platform
the step number three this is where most
people are at the moment
and they are slowly moving towards the
the number four which is around
intelligent digital twins one of the
applications for intelligent digital
twins is front running simulation it's
by no means the the only one it's just
the one that's front of mind which helps
explain the intelligent aspect of that
as well
when we start comparing digital Twins
versus intelligent digital twins there
are certain characteristics of a digital
twin and then there are characteristics
of an intelligent digital twin I've just
built the slides here again I'm not
going to go into into detail here the QR
code in the middle will take you to a
video which will go into a lot more
around the digital twin capabilities and
characteristics and the intelligent on
the other side
the one piece right at the bottom is the
anticipatory or the front running
simulation side of things
keeping with the the XM Pro product
within our intelligent digital twin
suite for those of you who are familiar
you're familiar with the data stream
designer the DS the app designer which
is the ad here and the subscription
manager of the SM here
the new one which you may not have seen
is around the AI side
rounding out the intelligent digital
twin Suite here
we'll go into that quite a bit of what
I'm going to walk you through you can do
without and I'll touch on some of those
areas where you can and then also touch
on the areas where you do need the the
newest addition here which would be on
the on the AI side
we have to stop in at the tip of the
iceberg where does it slot in so when
most people look at our digital Twins
and this is true for normal digital turn
platforms all the way through to
intelligent digital turns as well as
most people look at the top and they see
the uis there's actually a lot of things
that sit underneath
which is where the recommendations come
in and this is also where we slot in the
AI aspect as well the AI integration the
notebooks as well as how you
operationalize this for the agents you
still have everything else as you are
used to you've still got your data
stream and you've got your subscription
manager as well
we're going to leave the slides here and
we're going to jump into the actual
software itself and we're going to go
through a few examples of what it is
that I'm actually talking about here
so what we put together is we created an
app which will just allow me to walk
through the different bits and pieces
that we've got
let me just get rid of my slides
and we'll come back to to this
so again this is all driven from an
overarching framework which is what we
call our i3c
um and it's broken up into a few
different areas specifically with an AI
Focus for today we're talking around
Innovative embedded and augmented but
how do they all fit together where do
they all work together
we like pictures so we like explaining
how all of these fit together
as it is right now without having to
upgrade any accent Pro you can do
everything on the right here we call
that embedded AI
from the data stream designer how do you
bring the models in maybe you've already
got some models how do you
operationalize them and use them
everything else on the left here is some
of the new capability that we've brought
in with regards to Innovative AI as well
as augmented AI it's not required you
don't have to you can choose to use this
or not use this as well
so as we go into these different areas
what do I mean when I start talking
around Innovative AI we start going into
Excel Pro notebooks and there's two main
items in here the one is around the
ability to discover
so you'll see on the left we have a new
tool and that'll open up what we call
accent Pro notebooks
within here for those who are familiar
and comfortable you'll notice a familiar
environment being a Jupiter notebook
that we've got here
so this allows you to discover work
through and come up with the different
models Etc
any of the libraries that you can use
within a jupyter notebook you can use in
here this particular example I've got
here you can see this is around
predicting and the the beverage industry
using linear regression
as I build through this particular
notebook here you can see I'm going to
import certain libraries and it's going
to walk me through the different pieces
here so if there's a library that
I need for my machine learning or I want
to do some data Discovery I can load it
into the notebook not a problem at all
the other thing that as we work through
this that we've added into this is a
generative AI capability as well
what we mean by that is as I work
through this you'll notice at this point
here we've integrated chatgpt into this
environment as well so what that means
is you can actually use the chat GPT
to help augment and enrich what you're
looking for in here this example that
I've got in here what we're actually
asking
um chat GPT to do is to write me some
code on how to visualize the data that
I'm looking at I'm not an expert in how
I put this together so we're going to
ask and it'll actually generate
the items for me and if I go and run
that now you'll notice that it's going
to run and then it will generate and it
once it comes back it'll actually show
me the plot for the data that I'm
looking for
scroll a bit further down you'll
actually see what that looks like
if we keep going you can start
developing different models as well
if I come back here step one is really
what we call notebooks which allows you
to interact in an environment which is
familiar to you
you don't have to use them in here if
you do use them in here you can start
using some of the other features and the
bits and pieces that we've made
available as well if you have your own
models and you just want to use them and
plug them in by all means you can do
that already in the steps here
the second piece is really when we start
wanting to deploy models so there's two
main things that you can actually do
with the model
the one if I go into a different example
here is I can actually run this
it will generate the model I can help
discover that and the end result is I
can actually save this model to a file
that I cannot use inside the data stream
you can do the same thing right now
let's say you have a jupyter notebook
that sits outside X and pro you've
already doing Discovery you're already
generating models how do you just use
those models and bring them in you can
do that and I'll show you in a data
stream as well
the second thing that we're doing in the
prior example though is we're actually
fitting this model and passing it to a
repository and the reason for this is we
want to introduce a concept of
governance
and make sure that there is a framework
that you can actually deploy into and
that your models can live so that you
don't end up with a an Excel of machine
learning model sitting everywhere no one
knows it's got the latest version of
where it's actually sitting
if you have your own repository by all
means you can actually hook into and use
that as well this example here we're
just talking to to Mr flow as the
repository this is just one example it
is not the only example so if you've
already got one by all means you can
just plug that in and actually use that
as I continue to step through all of
this what it's doing is it's working out
the model it's working out the pieces
and then it is actually publishing that
model to my ml flow repository for me
you'll see it has already existed it's
going to created me a version 5 of that
model and now I can go into my actual
repository and decide when I want to
deploy that or elevate it to production
or not
so if we jump into that and again this
is just one example that we've got it's
not the only repository you can see the
one that we've just pushed through now
so number four is the one which is
currently in production I cannot decide
that number five is ready to go and I
can transition it to staging in a
controlled environment versus working
out well which is my live running model
which one isn't and how do you in a
governance structure move between the
different Frameworks that we're actually
looking at here
number four is currently published and
I'll show you now where that is actually
published and running
so if we come back here we call that
Innovative AI so if I come back to the
diagram that we're looking at here this
is the area that we are working in at
the moment is how do we take training
data how do we ask for specific results
that we are looking for and how do we
run that through a notebook you can't
augment that with chat GPT in this
example and then deploy that to an ml
Ops platform of your choice
you don't have to deploy it you can just
take the actual raw artifact which is a
result of that model and then use it in
a data stream as well
the
preferred or the more governance driven
process is to actually push that through
a
repository because then you get
everything that comes with the
repository as well
I mentioned you can do things right now
you don't need to upgrade or get all the
newest toys in the Box to to be able to
do this this is around the embedded AI
side so if I go into and this will open
up my data stream designer for me
you can do this right now you can drag
on this example has got a python runtime
running here which is running a model
this model was created inside the
notebook it was outputted from The
Notebook and then manually put into the
data stream and used and this is
capability that you can do as I
mentioned right now we call that how do
you use it from a manual perspective
what you'll also notice is there are a
lot of other items in the library
so if python is just being one of them
but let's say you want to use a more
standard algorithm like anomaly
detection or binary classification or
clustering
those agents already exist you can just
drag them on and use them right now
there's nothing stopping you you do not
need a repository you do not need to use
the Excel Pro notebook to take um
advantage of that particular capability
however if you do use a repository
and you actually want to integrate and
make use of that inside the data stream
what we've got here is we're performing
the analysis so this is using the Mr
flow agent so on the left you will see
here is a list of library
one of those being the ml flow
the Mr flow is talking to the repository
and here you can see the actual model
that it's talking to and everything else
is governed as well so I don't need to
remember the URLs I don't need to
remember logons passwords Etc I just
want to use that particular model and
actually just run it and execute it
again this is your governance that sits
around it it makes sure that the models
which you're using to do anomaly
detection Predictive Analytics maybe
you're using it to do forecasting or to
do front running simulation or just to
do normal simulation of current state or
past State you want to make sure that
you're running on the correct model
latest model and that you have control
of that you can do that inside the data
streams that we're looking at in here
so again if we come back
Innovative AI this is a really around
the ability to discover
go in through the particular notebooks
and then how do you use some of those
libraries in there the other example in
here is you can actually just create
models that do simulation so you don't
need to create models that do very
sophisticated
um AI number crunching and algorithms at
output you can do something as simple as
just simulation which is what this
particular example here is doing we're
creating a network we are then deploying
the same model to the repository again
you don't have to
There's an actual example of this one
running just the python itself with the
simulation on top of that as well
this one here is doing a remaining
useful life as well as a more advanced
prediction on the data coming in the one
that I just showed you is doing more
simulation so it caters to to both
there's no
you can do one and not the other
if we go into the augmented side
let me expand that a bit here so
embedded again you can do this right now
there's nothing stopping you from using
the current set of libraries and we're
expanding and adding to those all the
time
ml.net are some of those that you're
seeing in there ml flow when you're
talking to repositories maybe you want
to use some GPU enabled
um
algorithms from the the Nvidia Library
you can do all of that right now within
the embedded side of things
the Innovative is around the notebooks
but augmented is not just in the
notebook section over here
the augmented
AI you can actually use inside your apps
when you create and configure them as
well
what do I mean by that oh let's go into
an example
so I've got a particular app here and on
the right you'll see some
recommendations have triggered
these recommendations you can see
remaining useful life has been declining
and I can go a bit further down and I
can see remaining useful life has been
predicted to be below a certain
threshold again these were running
through the data stream and they can
either be Standalone python or any of
the other algorithms or read the matter
or repository and execute them as well
if I drill into that particular asset
it'll actually filter out my
recommendations for the two that I'm
interested in across the top you can
actually view the state of the model and
you can view the state of the data
that's flowing through the model so
actual versus predictive how are we
doing are we getting better we're
getting worse
if you apply this to simulation you
could have a button here that you could
click and it can actually show you the
progression of a simulation as it's
running through the uh the model itself
these recommendations here
you'll see above that there's a thing
called copilot copilot you can actually
bring into the application and use it in
here so if I was to ask it a question
let me just grab my prompt
and paste that in there
I can engage and talk with it and ask it
anything around this particular asset
that I'm interested in
this is using currently chat GPT so the
the data that I'm looking for here is
not sensitive in that regard however we
do have some OEM
Partners who have gone and taken as an
example Azure open Ai and they are using
that on their own data their documents
their manuals and they've deployed that
internally as well
this is just easier to to demonstrate
and show the concept of how you can plug
this in and where we are busy plugin the
different bits and pieces in so even
though this is using a chapter GPT API
here you could switch this out and put
azure's open AI in here as well a lot of
problem to do that
you'll see the the recommendations
coming down
you can go and expand that further and
actually pass these recommendations into
the assets themselves so here you can
see a discharge exception and I can
actually see that in this particular
Unity view as well I've got more space
for my co-pilot
um but I can still see the relevant
information that is applicable for me
if I drill into these recommendations
and I'll drill into this one as an
example and then I'll open a few others
as well
this is your quick start for
recommendations that you've configured
what I mean by quick start
so let me go all the way back here and
open up a different example for you
so when I say quick start you have
certain capability that is available out
the box you get it when you install and
you can just use it there's some other
capability that you can actually
configure within the platform as well so
we have a lot of different widgets we
have a lot of different applications
that you can just import we have a lot
of different templates
one of those templates is around
recommendations so you'll see at the top
I've actually got two sets of
recommendations on this particular page
for the Top If I go into my one example
this is the recommendations that most
people are familiar with XM Pro would
actually be looking at
this is the the Quick Start
you can't really change anything here
from an end user's experience or even
from someone who's configuring it you do
have some some control over the form and
maybe the triage instructions here but
that's about it you can't change any of
the layout you can't add anything to
this particular form maybe you want to
enhance and bring some other data in
some other capability in here as well
so how do you actually do that
if we go down the bottom
you can actually take all of that data
and make it available in different views
that your end users are interested in
consuming
same data presented a bit differently
but what this allows us to do is we can
now bring in let me hit my other prompt
there we can now bring in the copilot
into here as well and this is around dry
gas seals I can see my event data for
the event that has happened I can see
analytics so how often is this actually
happening is there something that
happens at certain times is it happening
when other events are happening as well
so we're dealing with a pressure seal
that is low we may have some high
temperatures maybe all of this is
related and there is a correlation
between these as well
however if we go a bit further down you
can bring the copilot in here as well
so now you've got a co-pilot which you
can deploy and use on an actual
application itself at an asset level or
maybe all the way up at a landing page
for your different views or you can have
a view where you can have it all the way
down with your recommendation data as
well
so now as an end user I can decide and I
can work through and work out based on
what I'm looking at based on what I've
asked and the responses I am getting do
I need to create a work request can I
capture any notes what do I actually
need to do with this recommendation that
I'm looking at here
and again this recommendation was
triggered from within the data streams
you'll see there is a run recommendation
if I go into the other example you'll
see there is a run recommendation as
well so we apply the same patterns in
our data streams irrespective or if if
you're using a model that is coming from
a repository
in this case this one is coming from a
repository or if you're using some of
the standard inbuilt
algorithms are already there or if you
just want to go and run your own model
and actually use that in here as well
they all follow the same pattern the
output of that you can pass to
recommendations and Trigger
recommendations as well because that's a
key thing for us is making sure that we
can close the loop on any of these
events that we find it's one thing to
just have a model it's how do you
operationalize the model and make it
useful
with the outputs that are coming
this view here
you can then look at the data you can
view any of the model information and
now you have a lot of different options
and how you want to react to that
if I close that
and we come back to this particular view
here
so to go through the the items that
we've just gone through
we talk around Innovation AI
embedded AI as well as augmented AI
embedded AI you can do right now
we have the libraries we have the agents
if you need specific agents for specific
algorithms they're pretty quick and easy
to create they need to talk to a certain
repository you don't want to use the one
that I've just shown here maybe you've
already got your own
those agents can be created very quickly
and deployed and used
on the augmented AI site
you can right now bring in
if I open that up again you can bring in
things like co-pilot into your
applications right now there's nothing
stopping you there's there's no
capability that you're missing to do
that the only thing that I would suggest
is make sure that whatever it is that
you want to use here
um you understand the privacy concerns
with the data again we are using chat
GPT here we are busy I can't show you
the the things on the Azure open AI side
because that's typically trained and
running on corporate data or customer
specific data for their customer
so this you would just swap out same
capability exactly the same mechanism
it's just what's sitting behind that'll
actually change for you as well you can
do that right now
The Innovation piece here this is where
you need access to the excellent Pro
notebook here
to be able to run and configure what
you're looking at we do provide if I go
back into the router we do provide a
quick start as well
the quick start for if you're not
familiar with all the different
capabilities the markup that you need to
use and how you can configure this a
quick start will actually walk you
through as well it's available as soon
as you have access to the AI designer
you'll find it in the list here and it
has a index and it'll just run you
through all the bits and pieces you need
if you're interested in some of the
other examples that we've got you by all
means reach out and happy to happy to
discuss how we can share and learn from
this as well
okay let's see if we've got any of the
the questions we do have a few questions
that came in there so first one does XM
Pro have models
um great question we get that quite a
lot when people see all the different
pieces and capabilities we don't have
specific models that we make available
we're not a data science scientist
company
however we do have certain algorithms
that we do make available so for
instance anomaly detection regression
um
classifications Etc what we do do is
give you the vehicles and the tools to
operationalize your own models whether
those are coming out of a repository as
we did with this piece here or whether
those are coming out of a data stream
and you go and configure everything
whether it's in python or even if you're
more comfortable in our script and you
want to use that you can do that as well
so we're more vehicle that allows you to
operationalize versus do we have a model
that does X on this asset
second question
um I did touch on it a few times but
just to to go through it is I have my
own models and repository do you I need
to choose yours being XM Pro
uh or can that you can keep your own you
do not have to replace what you
currently have you do not have to swap
that out and use ours you can just
integrate from ours into yours that'll
mostly get done inside the data stream
from the agent's perspective over here
again if there isn't an agent that will
connect to your repository of choice
it's pretty quick and easy to actually
create these types of Agents we can
create them on your behalf quite a few
of our partners do that as well or we
can even show you how to create these
yourself as well
the last thing I will leave you with
is just before we wrap up again thank
you for your time
um to today and we do have a webinar
coming out next month as well where
we're going to be talking about the 4.3
release
um the QR code here will take you
directly to the registration page if
you're more comfortable you can type the
link in and go from there if you have
any questions
um you can email me directly or just
sales at actionpro.com and again thank
you for the uh the time today and have a
great rest of the day
foreign
Last updated