>> Today I'm joined by Ashish to talk about
AI Builder and the Azure Open AI surface.
Let's see what it all can do in
this episode of Low Code Revolution. [MUSIC]
>> Hi everyone and welcome to
a new episode of The Low Code Revolution.
Today, I'm joined by Ashish and
we are going to talk about AI Builder.
Ashish, Welcome to the show.
Can you please introduce yourself?
>> Thank you Daniel. I'm super excited to be here.
I'm Product Manager in the AI Builder team
as part of Power Platform.
We're ready to talk about some Low Code AI.
>> That's really cool because I have
been seeing AI builder for a long time.
I've been seeing all the new models that came out and today,
you're going to show something about AI Builder,
also a new model, right?
>> Yes, we're super excited about
this new capability that we're bringing on AI Builder.
Again, let's show you some stuff.
>> Yeah. Let's dive into it. I'm really curious.
>> Let's get started, let's orient
people about what AI Builder is, just a quick overview.
AI Builder is no code AI capability within Power Platform.
Think of it as a collection of models,
a platform for low code and no code users.
It has a bunch of out-of-box models which you
can just use in your flows or apps right away.
You give it an input and it produces
an output in this AI generated output.
Its prediction coming from the AI model.
That's out-of-box models.
There are also customizable models
which you can bring your own data and
tweak them all to fit your business purpose. That's all for today.
All of this is then governed by an orchestration,
a validation feedback loop,
which creates this end to end experience of using Low Code AI.
Then on top of it is governance on how should these models behave?
Who should have access to the models,
which applications and flows can use these AI models.
Just the entire package for no code usage.
That's just quick intro.
Where does this all stack up within Microsoft AI Stack?
That's a question that we get asked a lot.
Again, on the right side you'll see a stack diagram.
The bottom of it is your Azure Machine Learning platform.
That is for pro data scientists to build your own custom models.
They have the full control to make peak performance of
their models and create fate for Business AI capabilities.
If we go a little bit on top of that stack,
we'll see Azure AI service,
which is also called cognitive services.
But really it starts to add cognitive dimensions to the AI.
You'll see vision, speech, language, decision.
Again, this is a language which
where a lot of pro developers play.
They still love working with APIs,
sometimes tricking the models.
They can still have a hands-on working with the AI.
But a more developer-centric rather
than data scientists centric layer.
A lot of data scientists work in this area as well.
Then the top layer is the no-code AI layer, which is AI Builder.
Now you'll see again
even higher dimensions around scenarios or document processing,
invoice processing, sentiment analysis,
and so on, so forth today.
This is where citizen developers,
no code makers play.
This is where AI Builder fits in.
And on top of it is
the enablement layer where AI Builder is enabling
everything else in the Power Platform Suite
from capability and product perspective.
>> Yes. If we look at the Power Platform suites,
if you look at, for instance,
Power Apps and Power Automate,
we have all those connectors already.
You can use AI Builder,
which is the easy on top
of Power Platform builds AI possibilities.
Then you also can connect,
I believe also to the Azure AI services, right?
>> Yes. There are hundreds of connectors,
over 900 connectives which fuel the power platform services.
AI Builder is going to monitor native AI capability built into it.
But then you can bring in things from
cognitive services and other places as
well that if you want to do that.
It's pretty flexible in that sense.
>> I believe also from the bottom layer even.
You can bring your own model to AI Builder.
There's tons of possibilities there.
>> That's true. Business users can work with
professional data scientists within
the organization to build, again, custom models.
We have something called bring-your-own model capability.
Using that professional data scientists
can build models and then bring it to a new code area where
business uses can now interact with that model and
use the benefit of that model
versus having to build it themselves.
>> AI builder has it all.
>> Let's go to the next slide,
which is going to be something
that we're super excited to talk about.
Again, AI Builder loves Generative AI.
We're getting into the area of Generative AI.
With this new capability that's lighting up,
you're bringing the Azure AI services.
Plus Azure Open AI services two AI Builder writes and
those models which do generate AI
are now available to large language models,
part of the AI Builder stack now.
With this, we are enabling a bunch of things.
First of all, prompt engineering.
One of our core principles has been,
how do we create simple and easy
to use interactions with the model?
Again, the model is pretty easy to use anyways,
because you are interacting with it using natural language.
But how do we even simplify that experience?
How do we remove all the clutter
from that experience and have it be more natural?
That's one of the things. Also, the
models and the output of the model.
How can we naturally and more
natively integrate with flows and apps.
That's something that you will see.
It's also bringing all the reliability of
Azure ecosystem on top of the Azure Open AI service.
You will see pieces of that as well.
Also being trying to be responsible and about that.
We built in a lot of content moderation capabilities.
Then the governance in our back and things like that.
Lastly, when this all goes prime time,
you will see this will be
very familiar licensing model around it,
which is Azure AI Builder credit mechanism.
That should sound familiar and easy to use as well.
>> It will be the same credits as
you use for all the other models?
>> Correct. You'll see new burn rate for this model.
Just like you would use as
a sentiment analysis model or a vision model,
this'll be another model in the overall stack and with a new one.
You'll still get your credits for it.
>> It sounds easy.
>> Yes. Again,
people ask, what can we do with this model.
The possibilities are endless.
I've just be careful with that,
that you could do whatever you want with that.
It's pretty flexible in that sense.
But over here,
what we are trying to do is just give
some aspirational things that people might want to do.
It's great with sentiment analysis,
it's great with translation.
You can actually summarize content very relevant.
We've also tried doing classification of text or content.
Your imagination is your limit toward less.
We would want people to play around and do
different things to create stubbornness.
>> I see a lot of the scenarios that we already
know from Chat GPT for instance,
and the other GPT models.
This sounds pretty logical.
>> Perfect.
>> Do you want to see how it works?
>> Yeah, would be lovely to see it.
>> Let's go over and take a look.
For people who are not familiar,
this is Power Automate.
On the left banner, you'll see all the capabilities and
there's AI Builder is an existing capability.
When you go to the Explore page,
you will see a new model,
a text model right about here.
Again, you could see all the models here document text.
In the text category, you will see a new tile light up,
which is Azure Open AI service.
When you click this,
you will see a simplified prompt engineering interface
or people are familiar with open-air K ground.
Then again, you can think of it as
a similar but simplified prompt engineering playground experience.
What you can do here is you can put
a prompt here and you can see response.
I'm going to put something here to play around with
and see how modelings behave.
When we tell the model things that we wanted to do.
It's an instruction and then model
produces a response as a result of it.
We've also populated the space with
some templates and predefined prompts
which appeal and should act as inspiration for business users.
You could summarize tags.
You can respond to our complain,
you can extract information from texts.
You can do sentiment analysis.
Let me pick another example here,
which would be, in this case,
we're trying to extract container numbers and
registration numbers from a bunch of texts below.
Let me send this to [inaudible] hopefully,
it goes out, I'm containing numbers and registrations.
These are predefined. Go ahead, sorry.
>> Yeah, I loved the templates, how they work.
It's really easy to just select
the template and then see all the instructions there.
That will help a lot of people with the prompting as
well because that's the hard part
in this whole scenario [inaudible]
>> Yeah. Sometimes you don't know what to ask.
Again, these should act as
a boilerplate prompts where people would try.
You can change these.
You can use this as an inspiration to do your own thing.
For example, you're not interested in registration numbers,
you just want container numbers,
you can make the change and get going,
or you can just remove all of this and start fresh.
That's possible too. But this
is just your playground, you're experimenting [inaudible].
You can take it in a flow,
you can use it in the maps, those are things you could do.
Let's go over to Power Automate and see how this looks.
What I'm trying to do is I'm trying to post a new topic on Teams.
This is what I do with my Teams every week I post a new topic for
conversation and you can
bring everybody together and discuss the topic.
What I'm trying to do here is create manually a topic,
a new conversation point.
I've added an OpenAI model in there.
You will see a new action light up within
Power Automate AI Builder which is create
texts and GBT on OpenAI service.
Then you can prompt it here.
You can tell it what you wanted to do.
In this case, I'm asking It create me a post which is
around 100 words on a given topic and be insightful,
creative, and be factual in your writing.
I'm just giving some instruction,
some context to the model to behave in a certain way.
I've given it the dynamic topic that I will
create here and it'll start generating content.
What we have also done is
we have made it easy to do prompt
engineering, lighten power Automate.
When you click "Create instruction", you see it here.
It's generating a topic.
I will bring a random topic to it and test if my prompt is okay,
put it here, and let it go.
But we're here as well. We've again brought
the same templates that you saw on the explored expedience.
People can again use those as inspiration to get started as well.
>> It's really helpful to have that
also in Power Automate
because normally when you are working on that,
you really want to try out.
You want to test. Then whenever you're ready,
what if you can just use it and maybe replace
some text by dynamic content field and then go on from there.
>> Absolutely, knowing that [inaudible] thinking as well,
that people would want to play it on an experiment.
The place where they are using the model or
creating the prompts and give them the ability to do
it. That's one thing.
One of the other thing that I wanted to
talk about is whenever you're
generating AI generating content.
Especially when it's going to multiple people
posting it externally or responding to customers or whatnot.
We're recommending to use an approval or human inspection step.
For example in this scenario,
I have added a human inspection step,
which is Pose my flow.
Given the opportunity for a human inspection,
I can say approve or reject
a given request and
look at the generated content and then make a decision.
But I don't want to approve or reject it.
Based on that approval.
The next set of actions in the flow will kick in.
For example, in this case,
my intention was to post it on Teams.
Once the approval is given,
then the automated generated content
will be posted on a Teams otherwise not.
You always have human in
the driving seat and you're using it as an Assistive,
a co-pilot capability,
but not full automation
because sometimes you don't know what the model would generate.
>> Yeah, I can imagine that you want to
have still control over it
because sometimes it will generate something that's not correct.
If you automate it, you're already done.
That's of course our part here.
>> Absolutely we're also in early days off these capabilities,
so we're continuously learning and improving them.
It's specially in the preview phases,
it's important to build trust with this capability
and use it in an assistive fashion.
See whether you can take it.
Whenever we add a new model in AI Builder.
It automatically lights up in Power Apps as well.
In this case, we are building a very small app.
You could take off this little party trick App,
which has asked me anything.
But the main thing is that you can add them
all right in your Power Apps as well.
When you go to data, you can say add data.
Instead of tables, we've got AI connector and
you will see the model up top here.
I've already added it, so you didn't see in the list,
but that's how you add them all.
The way you interact with it is an animal.
You call a predict function on it.
I using Power effects in here and I'm calling create,
text, GPT and I'm calling dot predict function.
I'm giving the question that I otherwise
had to this model and produce an output. It's as simple as that.
You can take it for a spin as well.
If I'll use the same query that I give to the other one.
Agile methodology to fifth grader.
You can ask the question, you'll hit
the wall and produces a response.
What we're also experimenting is in here,
we're showing this. You can add tokens.
You can add to the prompt,
you can tell it like a tone.
You want a professional tone,
you want a casual tone.
You want a certain style of writing.
You can specify the length of
that inlet so it's adding tokens to your prompt.
It's adding more contexts to your prompt.
Then when you generate,
you'll get a different flavor of the output.
>> That's really cool. You really leave it to the people to,
for instance, look at Tokens,
etc that they can easily add.
>> Yeah, and again, this prompt engineering is a new paradigm,
we call it the programming language of the new world.
People are learning it but at the beginning they'll need
assistance on how to create
better prompts and meaningful prompts,
it's more like talking to a person.
If you ask the right question,
you get the right response and how much context can you
add and can it produce the right output then?
>> Really cool. This was what you wanted it to show for us.
>> Yeah, absolutely.
>> Really cool. I really like the fact that you also showed
both experiences or actually all free experiences.
First, AI Builder, start from there,
then go to the separate products where you
can really use the model and how that experience is.
Thanks a lot, Ashish,
for showing us around the new capabilities.
I think the viewers would love
to get their hands on it when it's available.
Can you tell us a little bit about that?
Like, how is it going to roll out?
Can everybody just try it or is it
some phased roll-outs or how will that actually be?
>> Yeah, we'll start with a phased roll-out so people
can start using it today itself.
You can go to aka MS/ try [inaudible].
To try this experience is talking
with US and English only, you roll-out.
Audience and US will be able to
experience it earlier than everybody else.
Then it will gradually act
more uses and people [inaudible] build up the capacity.
Again, it's in preview.
That would be the natural progression
of the services as it moves along.
>> If people want to try it out and they are not in the US,
they can just create environment in US region probably.
I of course have to think about the data, etc.
but that's what they can do, of course.
>> That's good.
>> Thank you Ashish, and also thanks to our viewers.
If you liked this video, please like and subscribe.
I hope to see you at the next episode of the local revolution.
[MUSIC]