(mellow music)
Welcome back to Azure AI Essentials.
Today, we're going to take a look at how you, as a developer
can access the same AI capabilities that power Microsoft.
Microsoft uses industry leading AI models
across the organization in numerous solutions.
And we make those same AI capabilities available to you
to use in your own applications.
In fact, many of you may be interacting
with Azure AI every day without knowing it.
Take for example, X-Box. X-Box uses Personalizer,
one of Azure's cognitive services to personalize
the type of contents gamers see on their X-Box home screens.
By using Personalizer, X-Box is able to deliver
catered content to their subscribers
leading to a 40% lift in user engagement.
The Personalizer service functions
by ingesting information about a given user,
in this case, an Xbox gamer,
and provides content suggestions to surface back to them.
The ingested information includes things
like the user's last gaming achievements, online friends,
gaming style, or even the day of the week.
Normally, the process of selecting
what content to surface to users
would go through multiple cycles of data labeling,
model training and integrations.
Then the different content options would have to be tested
by being surfaced to the user
and measuring their responsiveness.
Personalizer takes care of all of that
in one easy to use service
because it is powered by reinforcement learning.
This means that personalize or works in real time
and is dynamic, with its models constantly learning
by observing how users respond to different things.
The service is very adaptable to any environment
with little to no need for human fine tuning.
Now let's dive into how you can implement Personalizer
in your applications.
To get started, you'll first need
to have an Azure subscription
and create a Personalizer resource
to retrieve your key and endpoint.
Then, install the Personalizer client library.
You'll now need to create a new programming file.
I'm doing this in Python today
but you can also do this in other programming languages.
Authenticate the Personalizer client
with your end point and key
and then add the following methods to your program class
to represent the content choices
that you want Personalizer to select the best content from.
Now we need to create a learning loop.
The Personalizer learning loop
is a cycle of rank and reward calls
based off of their respective APIs.
The rank API selects the best item from actions
based on real time information you provide
about content and context.
The reward API sends a reward
between zero and one to Personalizer,
based off how the action selected addressed business needs.
Each rank call to personalize the content
is followed by a reward call to tell Personalizer
how well the service performed.
To get their award score to send in the reward request,
the program retrieves the user selection,
assigns a numeric value to it
and then sends the unique event ID
and reward score to the reward API.
And just like that,
we've implemented our Personalizer service.
Similar to our other cognitive services,
Personalizer was built
and proven to meet global scale and security requirements.
It's why customers like Z-Tech, the technology hub
of the multinational drink and brewing company, AB InBev,
can deliver tailored recommendations
to mom and pop grocery stores in Mexico
through the Mi Mercado online marketplace.
They have seen a nearly 100% uplift and click through rates
for the Personalize products and a 67% increase
in converting customer interest into orders.
It's also why the NBA has partnered with Microsoft,
citing Personalizer as one of the key differentiators
for their partnership.
Now, let's take a look at another cognitive service
that's embedded into Microsoft products,
the speech to text service within Teams.
Let's say I'm here on a Teams call
and I want to transcribe my call for easier note taking.
I can do this easily by the simple click of a button
and the transcription will start.
In fact, speech to text will automatically format
and punctuate my call as well as use speaker recognition
to determine who said what.
Of course, there are countless other scenarios
in which the speech transcription service can be leveraged.
KPMG, a leading professional services organization
used Azure cognitive services and custom speech AI
to build a new risk analytics solution
for financial institutions.
Their solution uses AI to streamline call transcription,
translation and fraud analytics
cutting time, cost and effort by as much as 80%.
They were also able to get transcription accuracy
of at least 90%.
Now, let's see how you can use
the same speech to text service and your applications.
You'll first need to install and import the speech SDK.
To call the speech service using the SDK,
you'll need to create a speech config class
that includes your key and region.
To recognize speech using your device's microphone,
simply create a speech recognizer
without passing an audio config.
Instead, pass your speech config.
If you instead want to use a specific audio file
rather than a microphone, you can do this
by passing the file name as a parameter.
You can also either configure
your speech to text application
to recognize a single utterance
or you can use continuous recognition
if you want to control when to stop recognizing speech.
I've gone ahead and implemented continuous recognition
with a callback to stop when an event is received.
With everything set up,
we can start calling our speech recognition method.
To help visualize this implementation of speech to text,
I'm going to use this front end that's already been created.
Let's try it out.
Hello. How are you doing today?
Look at that.
It even punctuated my sentence correctly.
Speech to text can even transcribe audio to text
in more than 85 languages and variants.
Let's try this out in the Egyptian Arabic dialect.
(speaks in foreign language)
As an Arabic speaker,
I can verify that that worked correctly.
And wow, it's incredible.
The possibilities of what you can build
with Azure cognitive services are endless.
We're excited to see how you leverage
the power of Azure AI and apply these cognitive services
and many more to your own solutions.
Thank you for joining us on another episode
of Azure AI Essentials.
Until now next time.
(mellow music)