Azure AI is the cloud platform
offering the best tools
to ground, test, and deploy
both open source and frontier models,
and always do so safely.
As a former partner myself,
and now as the leader of the
Data, AI & Digital Applications business here at Microsoft,
I know just how important it is to stay current
with everything that Microsoft is bringing to market.
And so today I'm thrilled to show you
just how easy we're making this for you.
With the Azure AI Studio -
the place where a developer has
everything they need to bring
AI-powered experiences to their applications.
So you can see I'm starting with a selection of models,
including OpenAI's groundbreaking GPT-4,
which is available in the Azure OpenAI Service,
along with all of the other
OpenAI frontier models.
There are also great open source models
and thousands of models from Hugging Face.
And you'll notice it now also includes
the Llama 2 models from Meta.
After choosing a model,
I can see sample inputs and outputs,
try models out, and fine-tune and deploy models
into their own private sandbox running in Azure.
And I like to call this the AI version
of the old “File > Save As” move.
And I want to pause here because this is where
magic happens.
This means organizations
can quickly and easily
benefit from the power
of these foundational models,
yet they have their own
private instance of the model.
And why does this matter?
Because application usage and fine-tuning
of the model makes the model more effective.
And model effectiveness is the currency of AI.
And most importantly, as I deploy the model,
I can ensure it follows Responsible AI best practices
because Azure AI Studio has content safety built right in.
I'll come back to that in a minute.
And so with just a few clicks,
I can deploy a brand new Llama 2 endpoint
and start integrating it
into my applications straight away,
and it can be that easy, but
I'd like to take it a step further, because we know
in this new world of generative AI,
prompt engineering,
which if you don't know, is the process
of choosing the right words,
phrases, symbols, and formats to guide the model,
is critical to getting that great result
from the large language models.
And so to do this, I'm using PromptFlow,
which supports commonly used frameworks like
LangChain and Semantic Kernel,
or I can just use native Python.
And it works with multiple data sources,
both structured and unstructured,
so I stay in the flow of my development work,
never needing to move to other tools.
And we know that safety is paramount
and it is the most important priority for our
Azure AI platform. Using Azure AI Content
Safety again built right into the Azure AI Studio,
I can directly add
safety controls to the deployed model and choose
from a range of high, medium
and low sensitivity labels and other controls
for an even more finely tuned safety experience.
So now that I've got the safety controls
where I want them to be PromptFlow has tooling
for bulk testing to evaluate the results from the prompts,
and you can see how the answers are being evaluated
for how grounded the answer
is to the original question.
On a scale of 1 to 5,
I can also use built in evaluations
for things like accuracy,
relevancy, or coherence,
or I can even just create my own parameters.
And once
testing is complete, deployment is easy.
Creating the final API,
which includes
all of the power that the foundational model offers
with the additional value
from the prompt engineering and safety work
that I just added.
I can even use the built in testing chat window
to see the model
being queried via the new prompt flow.
And once deployed,
I can always come back here to monitor
how the model is performing
as the application is actually getting used.
Now, Satya also announced
that we've optimized
the Llama 2 models to run on Windows.
There's been amazing advances in Windows
hardware in the last few years
with better GPUs and now in NPUs
unlocking the power to run models locally.
When you couple that computing power
with the utility of the Windows terminal
and the Windows subsystem for Linux,
things like local support
for container-based development
and great tools like VS Code
and advanced tools like the ONNX
you have a fantastic place to do
AI development locally.
So here you'll see that
I have the 7 billion parameter Meta Llama 2 model
running locally on my Windows PC
via ONNX and Direct ML
to mill being called by C# one UI application.
As you can see, the model easily generates
a very detailed reply.
At Microsoft, we are committed to offering
the most comprehensive selection
of open and frontier models like Llama 2.
We're bringing together the best services
and tools available
to empower developers and organizations
to bring AI-powered applications to life easily,
safely
and at the pace our customers in this era of AI demand
Back to you, Satya.