(gentle upbeat music)
- [Speaker] Artificial intelligence
it's improving our knowledge and understanding.
It's delivering us insights to inform what we do,
and it's transforming the world far beyond
anything we've imagined.
As more businesses use AI
and expand the limits of what it can accomplish,
the complexity of designing, training, and deploying,
sophisticated AI and machine learning models increases.
With the rise in complexity and demands of AI,
comes the importance of AI infrastructure
that is optimized for compute heavy workloads.
AI requires purpose built infrastructure
and specialized compute capabilities
to train models and build applications efficiently.
Whether you're deploying natural language processing,
robot powered process automation,
or machine learning and deep learning,
the infrastructure needs to be capable
of scaling up to take advantage
of multiple, tightly coupled AI accelerators,
and scaling out to manage powerful servers
located across networks and in the cloud.
When scaling out,
we combine the power of multiple individual GPUs
into a large shared pool that can train and manage
large and complex AI models.
Combined with the impressive processing capabilities
of optimized host CPUs and GPUs
with their large capacity and high speed memory,
this architecture has proven to be extremely effective
at training models with large amounts of data.
AI solutions are used every day
across a growing number of sectors.
In healthcare, AI is helping professionals save lives
by training machine learning models
that analyze medical images for signs of cancer
and other major concerns.
AI tools are also providing second opinions
based on described symptoms and other analysis.
Groundbreaking healthcare solutions like these
are only possible through advancements in infrastructure
that can handle the massive scale required
for AI development.
High performing networking technology,
like Nvidia Quantum-2 InfiniBand,
enables communication between multiple nodes
necessary for distributing AI model training
on an infrastructure with GPUs connected through
high bandwidth and low latency network technology.
This means that your AI processes can be fast,
like really fast.
Speed like that is needed when you're
using AI in a busy automotive factory.
Like how a major car maker worked with Microsoft
to automate their quality inspections.
The company combined AI-based learning models
with industrial robots equipped with computer vision.
With the dynamic scalability available through the cloud,
the software for vehicle inspection can quickly increase
its processing demands by tenfold on short notice.
That's because Azure's cloud AI
supercomputing infrastructure
and end-to-end machine learning capabilities
give you the best performance, scalability,
and built-in security needed by demanding AI workloads.
Retailers who want to expand their operations
also have growing IT needs.
This costs time and money,
and traditional technology updates
can create limited flexibility.
Your AI workload demands shift rapidly
meaning you need an AI infrastructure that's elastic,
and able to quickly scale resources up or down
to optimize operational costs.
Having the flexibility to manage AI applications
isn't just desirable for a growing number of organizations.
It's a necessity,
but investing in large fixed hardware
and software upgrades can be costly in the long run.
Instead with your AI infrastructure now in the cloud
you have the flexibility to scale your compute as needed,
and even combine memory from multiple GPUs
where you need it the most.
Just a single high-end model,
like a large language model or LLM,
can run across thousands of GPUs at a time.
But you might be wondering,
"Where exactly do I start?"
Joint technologies between Microsoft Azure
and Nvidia are ready to train, deploy,
and manage your AI models.
Our comprehensive portfolio
includes Azure N-series Virtual Machines
powered by Nvidia GPUs in the cloud
creating impressive price-to-performance
returns on investment.
It's our full stack that enables you to
scale up and scale out no matter the challenge.
(bright music)
Find out more about AI-first infrastructure in the cloud
and how Microsoft and Nvidia can help make AI your reality.