>> Do you have time series data?
Data with timestamps and
important critical business information with that?
Well, you're not going to want to miss
this next episode of the AI show,
where we talk about Anomaly Detector.
[MUSIC].
>> Hello. Welcome to this episode of the AI show where we're
going to talk about the Anomaly Detector.
I've got a special guest, how are you doing my friend?
>> Great. Happy to assist.
>> Good, tell us who you are and what you do.
>> I'm Jun from China,
I'm working at Microsoft AI platform team.
I'm actually the PM of Anomaly Detector.
Grid service, Azure Cognitive Service.
>> This is a brand new thing, so why don't you give us like
a 10-second overview of what it is, 10,000 for level.
>> Sure, 10 second, right?
So Anomaly Detector is basically AI service app,
how the service, an API service that's going
to detect anomalies from time series.
>> I see.
>> Yeah. So time series is basically measures
of any metrics and with timestamp.
So that's what the service is.
>> So that's awesome. Can you give me a sense for what use case.
Because I think I understand what you're saying but can
you tell me what use cases there
are so I get a little more concrete in my head.
>> Yeah, make sense. Actually, I got a few here.
So we start with business metrics
because that's where we started from actually.
Business metrics is basically something like hey,
we got website and there's
visitors coming in every day
and there's also revenue of the website.
That's why this website exists.
Now, you've got those KPIs there and it has a timestamp,
maybe daily or even hourly or even
weekly and you want to check that metrics,
you want to monitor that metrics,
you want to know when there's something wrong with it.
You want to know it in time before this big loss,
that's a big loss to the revenue.
So we that, where's
the business metrics Anomaly Detector is trying to
address the way that when
something like Hopscotch call doesn't
work and that's why the AI is coming to.
>> So it's basically like if there's something happening
in your business that's not expected,
we want to flag that right as it's starting.
That way we don't get into a bad situation.
>> You don't want to know it after everything
is at a catastrophic failure.
>> So what other scenarios are there?
>> So business metric is just one common data that we address.
The other type of scenarios that we constantly face
and our customers came to us a lot is about IoT.
In industries like cars or oil
and energy and even some factory floors,
customers will deploy the sensors in the production line,
in cars and drones,
and those sensors will emit data,
especially at time series data and in metrics like temperature,
humidity or even vibration.
So all that data is basically
the metrics that you do want to monitor by human.
That's where we can also help.
>> So if a machine is getting too hot,
you want to know right away before you burn up
your machine or if there is a sensor
like for example you have a fence and all of a
sudden there's a spike in temperature,
that would be something you want to look at as well.
>> You want to look at or you want to check something to turn on
it before it's going to explode or something.
>> So as I'm understanding,
any data that it has
a timestamp would be a good candidate for this.
But I imagine that this is
a hard problem and there are some issues with it,
and this is why we've got to sort of.
What makes this particular problem hard?
>> That's a great question,
and that's why we shipped this product to help everyone.
So because all those scenarios that I've mentioned is
basically dealing with the challenge of
how to detect anomalies from time series data.
But in some sense to data by itself is just has two fields.
One is the timestamp and the other one is a measure.
There's nothing more about it.
So if you want to detect anomalies from that,
traditionally, people will set something like a threshold.
Hard-threshold is proven not working because was a high bar.
You will likely miss a lot of anomalies.
>> So you're it's set at say a flat number as
a threshold is what's been done before but as we go forward,
that could be a problem because you
might set the threshold too high,
too low and then you can still cause damage.
>> Yes, exactly.
The one challenge with threshold is business will change.
So one day this will work and the other day no,
it grows or a declines.
You need to reset everything. That's a big challenge.
Well, we are the AI guys and now in Iowa,
well, actually already some existing approaches.
Maybe I would say algorithms,
trying to adjust to this.
So as you can see in this screen,
we have listed, enumerate some really fancy charts.
But these are really the challenges,
these are the liability of real world data.
There are Chen change,
there are seasonality like this and there
are normal anomalies like Spikes and Dips,
and there are also linear pattern here.
So with all the different patterns of data,
the existing solutions, most of those are
trying to find out a algorithm that
one algorithm fits whole actually doesn't work.
>> As I'm looking at this chart,
what I'm looking at is the blue is
the actual timestamp data and the red dots are where we
know there are anomalies and each of these six scenarios
are indications of your data
might look like this and then it has to do this.
So it's a huge problem in that you really can't
tell initially where your anomaly
would be without an in-depth study of your data.
>> Yes, that's true.
>> I mean, it's pretty cool to see that
but how do we go about solving this?
What is it that we've done
to make this a little bit easier for customer?
>> So that's the exciting part.
Anomaly Detector was announced
actually earlier this year in March.
But this is another new service.
We have already been hosting or running
an AI-powered anomaly detection service
using Microsoft for several years.
So this means the anomaly detection
and the algorithms under the old days is
actually running with Microsoft and
product data for hundreds of product teams,
and that production data is helping
us to improve those algorithms and improve
the engineering efforts in the system and
now we are bringing that capability,
that knowledge to you, to customers,
to our customers and as the Anomaly Detector.
>> This is pretty cool because even though it's
a new product as of March,
this isn't a new thing to Microsoft.
We have all of this stored up knowledge and data
of what anomalies look like all of our various business processes,
and we're using that data to actually
help customers predict on
anomalies in their data; is that correct?
>> Yes. So today there a lot
of customers coming to us with
various scenarios and that
has challenged us in their business problems.
So like as you can see even with the oldest reliability of
data like the changes in
the existing solutions that cannot be addressed,
we're able to address.
So I got a few points here.
>> Firstly, it's about the variability,
all those that it has.
>> Right, that's the point. Yeah.
>> Yeah. One algorithm fits all is not working,
so we are coming with a pool of algorithms.
We got a lot of algorithms.
The innovation here is,
first we will detect your data pattern.
We find the algorithms that best fit
your data and to apply the algorithm to your data,
in order to have the best accuracy.
Also I like to mention this,
because this is my favorite part.
>> Right.
>> We have this SR-CNN which stands
for Spectral Residue and a Convolutional Neural Network.
>> That's really cool.
>> Well, that sounds really cool and that is cool.
That's a Nobel algorithm and
model that's recently developed by my team, Anomaly Detector team.
>> Fantastic.
>> There is a paper which is already accepted by KDD 2019.
>> That's so cool. So I'm looking at this and it's just
dawned on me that the problem
Anomaly Detection is the same problem with all of AI,
and that you have to first pick an algorithm that
might work for your data
and then you have to shuffle through those.
But you're saying that the Anomaly Detection service,
will go through and figure out
which algorithm would be the best one to use.
>> Yeah. And the best part of it.
You have to know that,
because we hide everything under the hood.
So the beauty of the services,
we try to address the requirements not
only for data scientists but also for developers.
Developers who can write code with C-sharp, Java.
So which means we actually shape
this as a AI service and every algorithm,
the complexity about the parameters
that you need to tune each algorithm,
are all hidden under the hood.
>> Fantastic.
>> What's left there is
this API and there is one parameter which we call it sensitivity.
Leave your bit flexibility there,
but still if you are just a developer,
not a data scientist.
You are able to empower
in building this into your application, no problem.
>> So can you show us a little demo? Maybe to get us started.
>> Of course.
>> Let us do that.
>> Yeah. So actually,
this is Azure portal.
>> Okay.
>> Yeah. We actually started with
something called the resource on Azure portal,
because it's API service.
But what you need is Anomaly Detector Resource
on Azure that's going to host the APIs.
So let me show.
This is a way that you create an Anomaly Detector Resource.
If you see this, and that's where you are,
and then input name, let me do this quick.
So far we have two regions,
West US to trans West Europe.
As we go into GA and there will be more, a lot more actually.
>> Got it.
>> So pricing tier.
If you are the first-time user,
you will still have this option with a free tier,
but I'm not. So I got.
>> You are not a first-time user?
>> No, I'm not. Otherwise I would not be talking about this.
>> Right.
>> Okay. Let me create, oops.
>> So this is just as easy as creating as Azure service?
>> Yes. Exactly. This is
the typical Azure Portal, Azure Resource Creation.
Once you've got this resource, there's some paths.
Let me switch to this Nobel first.
So what we hosted on the Azure Noble Platform,
is basically few Jupiter Notebooks.
The data scientists and developers both-like.
I'm going to show you how interesting and easy it is.
So yeah, you go to the Azure Notebooks.
First you need to sign-in.
Otherwise it will be read-only to you.
If you want to play with your own Notebook,
go to this project and clone your own instance.
>> We'll get these links at the bottom of the show notes.
So if you want to do this you can.
>> Yeah. Let me make another clone here.
You don't want to check this,
otherwise the project will be public to anyone on the Internet.
So while it's being created,
while it's being cloned,
let's go to the resource.
Let me see, where is it?
Yeah.
Azure. That's it.
.>> Looks like it was made.
>> Yeah. It is. So as you can see,
there is the overview and everything.
What you need to take note down is include two things,
one is this Endpoint, this West US too,
I already got this, so I'm not going to copy and paste.
But this is one thing and the other thing is the Keys,
you click here got this API key.
Because you are going to call the API with that.
Once you got your Notebook project here,
and this is clone of yours,
you can actually run on three computers free.
So with that, I'm just going to run it really quick.
>> Very good.
>> Yes, this is the Notebook.
All you have to do is paste our key here.
So we can do this actually, and the Endpoint,
this is correct but if you are creating Western U
remember to change this
and this is the previous result.
I can actually run and clean everything and
run again. "Run All. "
>> Fantastic. So basically,
if you want to get started quickly
and all you do is create the servers,
clone these Notebooks and you just run it,
and then you have something to look at to work on later on?
>> Yes, they're all those requirements
about the keys and
the endpoint it's all from the resource that you created on Azure.
Within this Notebook, you can play
with the parameter that I've mentioned,
the sensitivity, also the data.
That's saved side-by-side with the Notebook you can change that
and you can run on Anomaly Detection. Well, it's working out.
So as you can see,
these are anomalies detected from
the data that's sitting here. These are JSON.
>> That's really cool.
>> Yeah.
>> Another thing is you literally created the services,
cloned the Notebook, and so everything that you saw
happen literally happened right here which is pretty cool.
>> Yeah.
>> So where can people go to
find out more how can they get started?
>> Yeah, I got this slide here that
can help you really a
few highlighted links that have you start with.
So first go to
aka.msAnomalyDetector or search on
Bing for Azure Anomaly Detector.
You'll get to the overview.
Then we also have this community.
That's a Microsoft Teams group,
that we host for both external customers and
internal Microsoft guys and ourselves the engineering team.
Yeah. There's a form here you can go there and apply to join.
Finally, the Containers.
Well, I'm going to talk about this in our next episode.
>> Fantastic.
>> Yeah.
>> So thank you so much for spending some time with us.
Thank you so much [inaudible].
We have been learning all about Anomaly Detector
an amazing new service that you should check out today.
Thanks for watching and we'll see you next time. Take care.
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