>> You're not going to want to miss this episode of the AI show,
where we lift and shift anomaly detector down.
It's going to be pretty cool. Hello. Welcome to
this episode of the AI show,
where we're going to talk about the anomaly detector,
but with a special use case,
why don't you introduce yourself my friend.
>> I'm Chen from AI platform team,
and the PM of Anomaly detector.
>> Fantastic. So in a previous episode,
we'll put a link below so you can watch it,
we talked about the Anomaly Detector.
But there's got to be some challenges with running
it at a service like that. Can you talk about those?
>> Yeah. Because Anomaly Detector as we
introduced in a previous episode,
it's hosted API service and other Cloud.
The endpoint is West US tour or something right, that means,
your data needs to send over to the Cloud,
what she was going to send
over is time series, It's a lot of data.
So our customers actually has come to us with a few questions.
>> Right.
>> Let me go to the first one.
So first one is basically a production line.
A customer in the manufacturing industry came to us,
I was like hey, "We got this data,
but the data is like a tons of data.
We don't want to send that over to
the Cloud and wait for the latency".
We trust their service, their service will
run with good performance,
but the connectivity is really a challenge you there.
So we need something that's close to our data.
>> I see.
>> That's one of the challenges, that-.
>> That's make sense, if you're wearing like an IoT device,
it's outputting like let's just say you're
doing a timestamp every second.
>> Yeah.
>> This starts to add up if you have hundreds of devices,
and if you have to upload a gigabyte just to detect Anomaly,
just maybe downloading that will because you to get
into a sensitivity area
like it's too late the anomaly already passing.
>> Yes. Although, that we will
recommend aggregation before you send over the data,
but still that's a lot of data.
>> Got it.
>> Yeah, and then they say
the second customer is actually coming from the financial.
I'm not going to make up the MIM,
but this is a real case that a customer can towns like
we need to follow regulation,
follow laws that we don't want to send any sensitive data.
They can do masking, they can do anything,
but still it's sensitive financial data,
they don't want to send it over to the Cloud.
>> Right.
>> Although Microsoft and runs trusted Cloud,
but it's the lower ice has a regulation.
>> I had people just like in a hospital for example,
that we have laws in their country,
that says you cannot upload this form of
the Cloud even if it's safe. Right?
>> Exactly. Yeah, it's about sensitive data,
it's about personal information.
So you don't want to send that over to a Cloud.
>> Right.
>> The third challenge is coming from an oil rig,
it's from one of our ISVs,
Microsoft ISVs, came over to us.
I was like hey,
we got this application which is really cool.
Oil rigs solution, but we
already using your Anomaly detector APIs on the Cloud,
and we have everything that shoe shop called ready there.
Can we run it without connection to your Cloud endpoints?
Can we run it in our Rick,
can we run it close to our data?
I was like, "Okay.
Yes, so let's talking about in consistency, Cloud consistency,
because they are running the Cloud endpoint,
and they're running Azure Stack for their on-prem solution.
So which means they need to and they wanted
to have consistent code running everywhere.
>> I see.
>> Yeah.
>> So you talked about latency, you talked about,
I don't know if it was security but privacy,
and then lastly consistency.
>> Yes.
>> What are we doing to solve these issues?
>> So, that's the service that
we are shipping actually back in May.
We announced a public review of Anomaly detector containers.
>> I see.
>> So as Docker containers is going to address all those changes.
Then the thing about containers is you can run it and everywhere.
So like Anomaly detector containers,
is basically the same service to the same APIs,
with the same flavor of the host the Cal service,
there you can run in your container infrastructure.
>> Got it.
>> Those companies infrastructure or could be a Kubernetes.
So you can start with Kubernetes on the Cloud, that's AKS,
Azure Kubernetes Service, also Azure container instance,
and also Kubernetes cluster,
and that's running on your on-prem infrastructure like
Azure Stack or traditional clusters.
Also Dock of because,
if you have a laptop like mine you can
run the container there no problem.
So this is why that we shipped
the service in the same framework 3R container.
>> That's amaizing. So it's a Docker containers,
so anything to do a Docker container you can do with it.
Now, can you give us a sense like,
for example, how do I pay for this service?
>> Yeah.
>> How do I install it? How do I use it?
Can you give us maybe a little demo and rundown of how this works?
>> That's a good question.
So when we ship containers,
we were thinking about, "Hey,
there are cases when air-gapped infrastructure
that has no connection to the Internet
at all will want to run this container's.
However, we started with
this containers the version
that actually still require a connection to the Internet.
But this is all about the Billington in my tray,
that's no customer data going to the Cloud anymore.
So the way that or the experience
that our customers will face to run to use the containers,
the first step is always about
creating the Anomaly detector resource on the Cloud.
>> I see.
>> If you watch our last episode,
you will see that,
if you create a resource on Azure pothole,
there will be this API endpoint and this API key,
which you need to use the leverage is both, actually both of them.
API key is a key that we'll have
your container running and have us building the usage.
>> I see.
>> The API endpoint is where
your billing telemetry is going through the Cloud.
>> So if I'm understanding it right,
basically you're downloading the container
and the only information that's ever coming back to
Microsoft is the telemetry,
so that you know how many times it's been calls.
>> The billing telemetry.
>> Got it.
>> That's nothing like, hey your data or the pattern of the other.
No, we don't check that,
and we don't send over that to the Cloud.
>> That's pretty cool.
>> Yes. So with the resource and
the second thing you want to do is to
download the image because it's container image.
So far as we speak,
the container image is not on Docker hub,
not on MCR, Microsoft Container Redeposit Registry yet.
We actually hosted in private Azure Container Registry,
and so you need to go to this
URL aka.ms/adContainer to apply for the credentials to access it.
>> I see.
>> Once you've got that, this is not complex,
but eventually we will release the containers. But, with that you are going to run the container locally
with the key and the endpoint that you got from the cards.
>> It's almost like I'm trying to envision,
is basically like this service but running locally.
>> Exactly.
>> Okay.
>> Same service.
>> I see.
>> I'm going to show you later,
but this is how it works.
>> I think this is important to recognize that,
it's the same service,
the same billing structure,
you're calling it the same way that we
did because we thought we had a notebook that we use.
You basically change the endpoint to local host
8080 if that is true, and that's how it works.
>> Yes, exactly.
>> All right. Let's see it in action.
>> Sure, I'm going to show you a demo Container.
So the first thing you want to do is actually go to
the AD container form to submit your scenario,
and we'll review and it gives
you the credential to download the image.
With that, what you're going to do is,
I'm going to show my stickiness here.
So we will give you a password and a user name.
With that combination, you will want to use Docker login.
We have documentation on Azure technical documentation website.
So you can follow everything there.
I'm doing basically exactly the same thing.
Spin up another command window.
So this is Docker login.
You login to the Azure Container Registry,
that hosts the image.
The second step you're going to pull the image.
>> So this is basically regular standard Docker commands.
>> It is.
>> It's not anything special. Yes exactly.
If you are Docker guy,
you will know that this is not something that's new.
Well, there's something you hear now.
>> Yeah.
>> So as you can see,
if you want to run the container images that you
pulled with Docker pull of Anomaly detector,
we actually need to specify three unique parameters.
So these are the-.
Standard.
>> Standard Docker parameters.
Now, you're in need accepted the user agreement,
license agreement, this is
the billing end point that you
achieve from your Anomaly detector results.
Under the key, that's the way that we bill you.
>> Yeah.
>> You need to specify all of these three,
and with Docker run,
yeah, give me a second.
It's quite straightforward.
Now implication started on 5,000,
let's take a look.
Yeah, this is the container.
This is not any container,
it's Azure cognitive service container,
and that there's a slag which basically is
the API description and the test data point.
So here you can actually try it out.
Yeah, I'm going to try it out here.
As you can see, there's this input parameter which is PAM series,
this time stamp and a measure.
You don't have to specify the key here again,
because this Docker image will spin up with the key.
>> I see.
>> Yeah, I mean, if you're call the API
without a key you are wasting your work,
because all transactions build with that key already,
that's Docker under the hood.
Then let me click the "Execute" button,
and as you can see.
>> There we go.
>> It's loading yet.
This is the result after the result response body here.
There is a few things,
maybe you want to notice,
does the expected value margin,
a margin lower margin and whether the last step point is Anomaly,
because I'm running this against the slash last API.
We will talk about it in a our later show later shows,
but this is how it works,
and it works exactly the same way with
the Cloud endpoint except for this.
>> Yeah.
>> You're calling the local endpoint.
>> That's awesome. Well, thank you so much for doing this,
where do people go to find more?
>> Sure. So always start with this AD container form.
You will get the credentials,
then we review pretty fast.
So you feel the form and gather credentials, pull the image.
Then if you need help,
if you want to chat with the engineering team and
the top chat with other members in the community,
we have Microsoft Teams Group.
>> That's cool.
>> Yeah, you can always click in apply and join it.
Finally, if you haven't watched the last episode and you can
watch that or can go to aka.IMS/anomalydetect,
or search it on Bing to get
an overview and
the technical documentation about Anomaly detector.
>> Awesome. Well, thanks so much for spending
some time with us, thanks so much for watching.
>> We've been learning about containers
using Anomaly detector on containers.
We'll see you next time. Take care.