(upbeat music)
- Hi everybody.
I'm Karl Podesta.
I work as a high-performance computing technical specialist
for Azure.
I'm happy to have you with me today
to listen about some of the challenges
our customers in manufacturing and engineering experience
and how we can address some of those challenges
with our platform.
So manufacturers are being asked
to visualize real-time products
and product performance from anywhere.
And that goes for our situation now
with COVID-19
and many of us are working from home.
And the expectations are still on us to work
as we have been doing before, from anywhere.
We also need to understand what's happening more
with physical products
and use things like digital twins
to really understand
what could happen in those products
and to really optimize the products a lot better.
We also need to incorporate feedback from IoT.
So devices that are now everywhere,
and information that's coming
and data that's coming from those devices,
we need to incorporate that data into the simulations
and into the work that's happening
so that we can develop better products
and understand real world effects into
the products that we're gonna design and improve.
And all of this has been asked to happen
a lot quicker than ever before.
You need to do this faster.
But challenges can really hamper our ability
to do all of that.
So we typically are working with some sort
of fixed system.
We have a workstation under our desk.
We have a bunch of computers and a server room somewhere,
and it's been an investment that we've made,
but suddenly,
a new workload has come in
and we need to do it faster than we need to do it before.
We don't have the capacity to do it.
And it's an opportunity that's been missed.
And then other times maybe the work is not been
as busy as we've expected it to,
so a number of projects happen together,
they often finish together,
and then some of the resources
that we've made investments in
are just sitting there not being used.
Again, another missed opportunity.
And again, how do we collaborate better
amongst all of this with our colleagues
spread across the world,
spread across different locations?
So we've really got a geographical disparity here
going on as well.
All of these obstacles together
really make it difficult for us to be able
to do our work.
So in addition to those challenges,
transformation of our business is also requiring
a next level of computing power.
So some of the items that you're seeing on the slide
are really illustrating how we have more data
than ever before,
how we will have more data than ever before,
and how we're also expected to learn
and incorporate that data into the simulations
and into the work that we're trying to understand.
So we believe that that transformation
can also happen through simulation.
This is a real opportunity,
especially with the cloud,
for us to bring simulation to everyone, everywhere.
And that means that we can also enhance
the traditional factory type of model that we're doing,
the traditional products that we're developing,
with these new digital capabilities.
We can really develop new products on top of that
and monetize on the back of that as well,
thus creating new value, new business for us.
And this is also what the transformation is really about.
So combining high-performance computing
and simulation with all of the other capabilities
of the cloud,
including IoT, artificial intelligence,
and machine learning.
This is the opportunity that we all have.
So thinking about the engineer's workflow,
we might start off using a workstation
where we have to develop the product initially
to do some pre-processing,
to develop a 3D model and interact with it.
Then spinning off to a simulation or an analysis
or multiple simulations or analysis
that may even take many hours,
many days to do.
And then we're coming back maybe to the workstation
to analyze the results of that process.
Maybe after all of that has been completed as well,
we're then moving on to developing product brochures
for that.
So maybe doing some rendering
of what the final product may look like
and publishing images for customers or colleagues
to look at alike.
But how about integrating deep-learning machine learning
or artificial intelligence techniques into that?
So what about all of the data
and all of the simulations we generated?
Are there smarter ways to do that?
And could machine learning really help us?
Integrating that into the workflow
that we've just described
is part of what can be done together
in the cloud.
So how can Microsoft and Azure, in particular,
help some of this transformation?
Well, it's different in terms of providing on-demand,
extensible, and scalable resources
to aid that transformation.
And all of this together with unique capabilities
that spits in Microsoft Azure.
So when we talk about being on-demand,
you wanna be able to scale up that compute
and simulation power when you need it,
as much as you need it,
wherever you need it.
You also want it to be extensible.
So you want it to complement the investments
you've already made in systems on-premise.
Maybe you only need this power for a particular project
or a particular simulation at a given time
and not for the rest of the year.
Another capability is it being scalable.
So you really want it to be able
to scale up and down easily.
The whole thing has to be easy to do,
and it has to leverage all of
these advanced technology capabilities
that we have together in Microsoft Azure.
And that includes at the operating-system level,
at the hardware level,
at the application level,
and of course, through our partnerships
and vendor ecosystem that we work closely with.
So here's a suggested path that you might be able to take
to make this happen.
So one of the things that you can do
to get started is to simply
lift and shift or clone to cloud,
and that involves taking applications or data
that you've already got,
spinning up some resources in the cloud,
and getting them done in that manner.
So this can already bring some benefits to you
in terms of geographic reach
and scalability, and choosing the type of machines
that you want.
And this really works for some of our customers
as things stand.
Then as you start to develop things a bit more,
you may really need to leverage
the existing investments you've made,
have a hybrid workflow
that encompasses storage compute capabilities
that you have already developed in your site,
and really expanding those to the cloud
as part of a whole workflow.
You can go on further and really do
the whole workflow in the cloud.
And that includes pre- and post-processing workstations,
multiple machines to run the simulations,
and capabilities such as machine learning,
IoT afterwards
to be able to develop and leverage more value
from the simulations that you've done.
And finally, you can also leverage
unique capabilities in Azure
to cloud native-style workflows.
So really it's about choosing the right workflow for you,
for your users,
for your situation.
Let's take a closer look at one of our compute offerings,
especially designed for HPC.
The Intel Xeon Scalable processor,
codename "Skylake,"
used with the Azure HC series,
is designed to meet the demanding requirements
of HPC in the cloud,
along with pre-installed and configured
tuning management software.
Users can take advantage of a common code base
across on-premises and public-cloud infrastructure,
including Intel hardware acceleration technologies,
like AVX-512,
and common HPC management and optimization software.
You can have confidence that
all of the Intel components you'll use
are optimized for maximum performance.
And that includes storage, interconnect,
compute, and memory.
Beyond the hardware components
of compute, storage, and networking,
there's unique capabilities in Microsoft Azure
to be able to leverage these in an easy way.
Two tools that we're highlighting today,
that are also free to use,
are Azure Batch and Azure CycleCloud.
And these are both methods you can use
to automatically create clusters
and machines together in the cloud,
turn them on when you need them,
turn them off when you don't need them,
and to do so automatically.
So whether you prefer to interact with this through code
or whether you prefer to have a nice interface
to be able to do it,
then you can share with users who are unfamiliar
with using the cloud,
but familiar with using clusters and workstations,
then these are two tools that are a great choice
that you can also integrate with your workflows.
Microsoft Azure is built for HPC.
Only Microsoft has all of the pieces of the puzzle
to fit together
to help you in your journey to the cloud,
wherever you may currently be at.
This includes a comprehensive portfolio
of capabilities, compute, storage,
networking, infrastructure as a service,
platform as a service,
software as a service,
and supercomputers-style capabilities
to meet your simulations, whatever scale.
We also make it easy to transition,
so we've a number of native tools
to make it easy to spin up these resources,
to put them together into workflows
that includes hybrid capabilities,
which are on-premise investments.
We also have intelligent cloud capabilities.
So you wanna join and combine
high-performance computing
with AI, machine learning, and IoT,
really to leverage the value that the cloud can bring
by combining these solutions together.
We have an extensive partner ecosystem,
including our partner Intel,
who we've highlighted here today.
And finally, it's the investment in HPC.
And this includes at all levels of Microsoft
from engineering, customer support,
sales, development,
to really go all in on HPC in the cloud.
Azure is productive, hybrid, intelligent, and trusted.
We hope that today we've helped you understand some
of the challenges that manufacturing
and engineering customers have
and how we're able to address those with Azure
and Azure high-performance computing solutions.
Thanks for listening.
Thanks for your time.
Take care and hope to see you soon.
(upbeat music)