CHLOE CONDON: Hi, I'm Chloe, a developer advocate
for Google Cloud.
In this video, we'll show you how you can accelerate your AI
processing with NVIDIA GPUs on Google Cloud,
even when you're subject to strict regulatory requirements.
We'll start with a quick review of the common challenges that
are faced by regulated customers when moving workloads
to the cloud and show you how Google Cloud's Assured
Workloads makes this process simple.
Then we'll show you how it's possible to use
NVIDIA's technology for AI acceleration
in combination with Assured Workloads
and walk you through a demo of a deployed solution
so you can see it in action.
Ready to learn how NVIDIA and Google
Cloud can help you address your regulated AI needs?
Let's get started.
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Before we dive in, let's do a quick review
of Assured Workloads.
Assured Workloads provides Google Cloud customers
with the ability to apply security controls
to a specific environment in support of compliance
requirements without compromising the quality
of their cloud experience.
All organizations have to do is choose
the right regulatory framework, and Google Cloud
will put the necessary cloud controls in place
to support your chosen compliance
requirements across the globe.
For a more in-depth overview of Assured Workloads,
be sure to take a look at our video,
"Introduction to Assured Workloads."
To accelerate specific workloads on your VMs or containers,
such as machine learning and data processing,
Compute Engine and Kubernetes Engine
provide NVIDIA graphics processing units
that you can add to your virtual machine instances
and Kubernetes clusters.
NVIDIA Accelerated Computing on Google Cloud
provides a suite of accelerator options for various AI needs.
For large-scale AI training, NVIDIA A100 GPUs
are available on GCP with up to 80 gigabytes of GPU memory
per chip.
This includes the ability to attach 16 A100 GPUs
on a single VM machine shape, the largest single-node GPU
instance from any major cloud provider on the market today.
For AI inference, NVIDIA T4s on Google Cloud
offers strong price performance to deploy AI applications
into production for low-latency needs.
Customers are also able to deploy NVIDIA AI solutions
onto GCP from NVIDIA NGC, a portal of enterprise
services, software management tools,
and support for end-to-end AI and digital twin workflows.
As AI continues to change and disrupt
the way we build and interact with technology,
running AI workloads in highly-regulated environments
can feel like a daunting task.
Many AI services such as infrastructure, platforms,
tools, and solutions exist today but may not
be available in cloud infrastructure that
supports highly-regulated customers and workloads.
This has forced many developers into situations
with unpredictable pricing or limited access to the latest AI
technology.
Now, thanks to NVIDIA GPUs and Assured Workloads,
you can use Google Cloud and NVIDIA's AI technologies
to help solve your business problems while remaining
compliant and secure.
And with a public catalog of enterprise-ready AI software
and solutions, you'll have access to frameworks, SDKs,
and ready-to-go models to help you build the custom AI
solutions you need.
And with one-click deployment through Google Cloud
Marketplace, NVIDIA AI Enterprise
makes it simple to deploy AI solutions
while remaining secure and compliant,
giving you access to the NVIDIA AI Enterprise software suite
as well as access to dedicated support anytime you need it.
With this greater access to technology, regions,
and resources for regulated workloads,
using Assured Workloads in combination with NVIDIA GPUs
can help tackle use cases like anomaly detection
manufacturing, predictive maintenance, as well as image
classification tasks, allowing you to quickly and easily build
and run solutions while remaining secure, compliant,
and with reduced cost and risk.
Thanks for tuning in.
For more information, check out our documentation,
or take a look at the links provided in the description
below for more info on how you can use Assured Workloads
and NVIDIA for your regulated AI cloud needs.
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