>> You're not going to want to miss
this episode of The AI Show where we
introduce QnA managed: now in Public Preview,
imagine a scenario where you can have all of the questions
answered automatically just by
literally pointing QnA Maker to a link.
You're not going to miss it. Make sure you tune in.
[MUSIC]
Hello and welcome to this special edition of The AI Show.
We're going to look at something called QnA managed.
We've got a special guest,
Neha Rajput, Program Manager.
Why don't you introduce yourself and tell us what you do?
>> Hey. Hi, Seth, nice meeting you.
Hi, guys. I'm Neha Rajput.
I'm a Program Manager in QnA Maker.
I particularly handle
the fundamental relevance part of the QnA Maker.
>> For those that don't know what QnA Maker is,
can you give us a little rundown of what it is?
>> Sure. QnA Maker is
actually used to create a conversation layer over your data,
the USP of the product is,
like it writes to zero or
no code effort where you can look in few clicks,
you can create a bot out of your data.
Your data can be present in any format.
It can be either semi-structured or structured.
It can be like a public URL,
PDF or SharePoint doc.
You can actually add personality to
your bot as well like professional,
things like professional, witty or friendly.
These are available in total eight languages
and we support almost more than 50 languages,
actually, 53 languages with
all the languages that Azure supports as of now.
The whole service is quite easy to scale.
>> So that's awesome. Let me ask
a couple questions because this to me,
I think it's kind of an amazing thing,
but it went really fast, let's see if I understand.
So basically, QnA Maker will take from your FAQ pages,
support websites, manuals, whatever,
and it will create a bot for you that
can answer any of those questions. Am I getting this right?
>> Yeah, exactly. You can actually
deploy that bot on the channel you like,
either Skype, Teams or all the direct link.
As we support are all on your website.
>> This is pretty amazing.
Just on its own,
can you tell me what's new because obviously, what
we're here to talk about the new parts of it.
>> Yeah. Sure. I'm quite excited to share with you that.
This is QnA Maker managed that is going public preview now.
The basic part of this QnA Maker manager,
and why we came with this whole thing is to provide
a simpler version of
resource management and resource creation to our user.
Now, you need to manage or create few resources.
I'll show you, I'll present a demo, where I'll show
you like how the number of resources get reduced.
Another thing is end-to-end region support that we're providing.
This is especially for those customers who have geo restrictions,
like Garmin firms or banks where they wanted end-to-end the
whole service to be present in one region.
That is another thing that is coming along in QnA Maker manage.
The third thing is now because we have changed our resource model,
now, we are supporting deep learning ranking border.
Whereas in current GA,
it's Machine learn models that we are supporting until now.
You'll see an enhancement in
the relevance results of our queries and which will be
horizontally across all the languages
because this model is multilingual,
it's not specific to any language.
Last thing is like a wow feature that we are reading,
which is MRC based on suspended action.
What it does is currently
whatever you store in your knowledge base,
when the end-customer queries,
they get that answer as a result of the query.
What this system does is this deep learning model,
it's able to detect if there is a precise or
a short answer visited onset visage itself.
Instead of returning the whole passage,
you can actually turn that start precise answer to the query.
I think I'll provide a demo of
this also how it differs from the current GA.
>> This is really, really exciting.
I'm excited to see,
can you show us how someone would set this up or how it works?
>> Sure. Most of you must know, but yeah,
just to give a quick overview,
this is our QnA Maker portal.
So how people normally work is they go to create a knowledge base.
What they do is they create the service.
First, we'll go to "Create QnA Maker service".
This is exactly similar as it is in current geo
so you will not be able to
differentiate between anything till now.
The moment you click on this "Create QnA maker service",
you'll go to the Create laid on Azure portal.
I'll just show how the current GA looks.
So this is the current GA form where
you get to see a QnA Maker resource,
then "Azure Search", then "App Service",
and then "Application Insights".
All in all, three services that customer
need to take care of as of now, in GA.
They need to decide the pricing tier for
all the three services based on their scenario.
It requires some amount of analysis
at the customer end but if you select,
"Manage", you see the form below has changed.
Now, there are only two services that you need to think of.
One is QnA Maker, resource and another is Azure Search.
So what we have done is we have merged QnA Maker
and App Service together and moved App Service
out of the user subscription so that we
can deploy state-of-art models, [inaudible] models,
and it's not dependent on the customer,
like how much they are able to scale up or down.
So it's quite easy for the customer to avail
what's available or what's best in the market.
Other thing that I said was the end-end regions' support.
As you can see, there are three regions.
I'll center US, Australia,
East, North Europe that we are already supporting.
The plan is when we put GA around [inaudible] support all regions.
I've kept this form pre-filled just
for the sake of just to say it one time.
>> Sure.
>> The other thing is our Azure search location,
here you should select whatever region
you're selected above just to keep the region same,
you click on the "Legal Terms",
you click on "Review" and "Create".
Now this is how your resources get created.
People who have already used QnA Maker must know.
As of now, you see five resources.
QnA Maker resource, App Service resource,
App Service plan, Azure Search Service, and Application Insights.
But here, there are only two resources
that you'd need to think of.
All you need to manage.
It's quite easy for the customer
and it produces those pin points at their end.
Other thing is like,
if you click on this "QnA Maker manage preview",
this resource of type cognitive service,
and you can see the location is same now,
so it's supporting end-to-end region support.
If I click here and if I go to the "Configuration",
you can actually bring your own index,
you can update your Azure Search, in case you already have
Azure Search servers that you want
to interact with QnA Maker Resource.
So this has become quite easy with this UI.
If you go to Encryption,
now, we support customer-managed keys through it.
So this provide an end-to-end encryption to the data of the user
and the question and answers
are also encrypted when they are stored in Azure Search.
If I go to "Networking", I'll just say, "Okay".
Here, you can also provide private endpoint support if
people need their own private endpoints
between QnA Maker Resource and Azure Search.
So that is also supported through this portal.
Now, let me directly jump to the QnA Maker portal back.
Let's say, we created this old resource.
I have kept almost all the information prefer.
What you see here is a new option.
This option doesn't come in the current sheet.
The reason being currently,
our language setting is specific to the service.
But with this QnA Maker Manage,
we are making this setting specific to that knowledge base itself.
So customer has a flexibility if they
want to have multilingual service.
So they can create one knowledge-based by language.
Initially, it was not there.
I mean, in current service,
you need to create a different service for each language.
That is really beneficial for people who
want to create bots in different languages,
but they don't have that much data,
so they don't need to create different services for that.
This is something that is new coming in QnA Maker Manage.
All other things in the form remains same.
Here, to upload the data for this demo,
I have used this public URL.
Let me show you what's there on this page.
It's actually a COVID FAQ doc,
they are commonly asked questions and there are answers to it.
I actually just clicked on this "Create your KB."
In few minutes, it creates a knowledge base page for you.
This, again, is very similar or
exactly similar to what we have in current GA.
You can see the source from which it
has extracted the question and answers.
Then you can see your question and answers here.
You can also modify your question and answers, like
you can add Bold, Italics,
and several markdown features that are available.
The other thing that I really wanted to show was
the MRC-based answer span detection.
If you click on "Tests" when you want
to test how your KB is working.
As of now, when you ask the question,
there's a simple answer to invert.
If I ask, "How many hours symptoms of COVID appear?"
Now, what you see is a short answer also.
This is the answer passage.
So currently, in GA you just see this whole passage,
how it is stored exactly in
your knowledge base that I just showed.
This answer, 24 hours is being picked up from this passage.
This is just to give a feel to the customer how
the short answers will be coming up if they want to avail that.
It's like a simple check.
It's like a simple pedometer that they can
switch on and avail this.
There are different templates present,
where if you want really short answers to come up,
or if you want both answers to come up,
or if you prefer just the answer passage.
Those kinds of functionalities are also available.
Let me ask another question.
Let's say, "How much
distance should we stay away from people?" So yeah.
If you see there's a short answer again
coming up which is 1.5 meters,
which is, again, being picked up from the answer passage.
It's quite easy to get the precise answer,
it's quite helpful for the people there who
had this kind of scenario where the answer passage used to be huge
and they used to have a proper content Dev team to manually curate
these kinds of question and answers pair
to provide a precise answer to the end-user.
This is the only difference between
the QnA Maker banished
management and authoring and the current service.
This is just to provide you how this feature will behave.
All in all the relevance and
the improvement to the relevance that happens through our
transformer-based [inaudible] that people will be
able to see once they migrate to
the Public Preview because it has improved
the relevance horizontally across all the languages.
So that's how you can test
your knowledge base and then you go to publish.
You click on "Publish",
it would take a minute or
two and it will generate endpoints for you.
>> While it's doing this, this is super impressive.
You've literally just uploaded a PDF, it read it,
and now, you can ask it any question,
but it's not just going to bring back the ginormous paragraph.
We also have some intelligence in there to pick out
the exact short answer. Is that right?
>> Yeah, exactly. That happens on
runtime and actually, that's quite helpful
for all the people who want just the precise answer
when they're talking to bot instead of reading the whole passage.
>> Yeah, that's really cool.
So what do you got going on here?
>> I was just showing like this is how you can simply create bot.
So I just got my knowledge base published,
I got my endpoints.
Now, the process from here remains
exactly the same hubs in GA.
You click on "Create Bot",
you go to Azure portal.
You see their direct lines in the channels where
you can deploy it to and you can get it on themes,
guide or whatever you want your bot to be.
>> This is amazing.
I can't tell you how many times
I've had to do literally just going like,
someone asked me a question and I just literally have
to put a link in the doc and say,
"Go here, and if you do the hashtag,
it takes you right to the H1 tag."
But this will automatically do all of that
for you with not a lot of work.
Where can people go to find out more?
How can they learn more about the service?
>> We're going to have our public blog out soon.
I'll provide the link to the public blog and
public documentation as well where you can see
all the new features that are coming along in the QnA command.
>> Well, Neha, this has been amazing.
Thank you so much for spending
some time with us and thank you so much for
watching and learning all about
QnA managed: now in Public Preview.
Make sure you read the link, we'll put it below,
and get started on saving a lot of time by using QnA Maker.
Thanks for watching and we'll see you next time. Take care.
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