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Hi I'm Ted Way I'm a program manager on the Azure Machine Learning team.
Today I'll give an overview of what Azure mail is, what's new.
And do a demo on how you can easily create a machine
learning experiments and consume it as an API.
To kick all of this off we need to understand why Machine Learning is
useful in the first place.
It's important because it can help you tell better stories with
the data you have.
Take this in Crook, a company that builds and maintains elevators.
They're installing sensors on their elevators and
that data is being collected.
They could tell the What Happened story in the form of reports.
Such as, in the past three months an elevator broke down in
downtown Seattle.
This story is about what happened in the past and is somewhat useful.
Now they might be able to tell the Why Did It Happen story,
with interactive dashboards.
You can then investigate and see that a pump broke.
These pumps are supposed to last ten years.
And those brand new two years ago, so nobody thought it would break at
least you know why it broke down though.
With Machine Learning you're now able to tell the what
will happen story.
You can take that sorts of data, run it through a Machine Learning model.
And predict whether that part will break down in the near future.
Now you know that the pump is acting strangely and
will likely break down.
And that ultimately helps you tell the What should I do story.
Because then you can replace that pump before it breaks and
make sure that elevator runs smoothly with no problems.
This is possible by creating a machine learning model in Azure ML.
Which is a fully managed cloud service that enables you to easily
build, deploy and share predictive analytics solutions.
You can bring in data that's stored in Blob or table storage
in a Hadoop cluster, a SQL database, or a NoSQL store such as DocumentDB.
Train your model with the data and the Machine Learning Studio and
deploy it as an API on Azure
is now callable as a web service that clients can use.
It's easy to create your web service as I'll show in a little bit.
The algorithms available to you are the same that we
use within Microsoft.
And we also make use of the innovations and
open source such as Ora and Python.
You can easily deploy the model with one click and
have it accessible as a Rest API.
So we've added a lot of new modules for text where you can do language
pre-processing and detection, N-grams extraction and LDA.
Azure Machine Learning is also available in new regions in addition
to North America, Europe and Southeast Asia.
You can also use Azure ML in Germany Compliance and
security is very important.
And we are HIPAA compliant,
EU Model Clause compliant, and ISO 27001 and 27108 certified.
We offer encryption at rest for your data.
And the Cortana Intelligence Suite Gallery now supports custom modules
and has a new section for videos and learning.
Now I'll do a demo showing how you can easily build a Machine Learning
model and operationalize it.
Cortana intelligence gallery is a great place to get started in
building your NTEN solution for advanced analytics.
Just go to cortanaintelligence.com and then you can see here if
you're interested in specific industry verticals such as retail,
manufacturing, banking.
Or healthcare or end-to-end solutions,
you can just click into them.
For example, let's say I wanted to do vehicle telemetry analytics.
I can just click into the solution.
And then by clicking the deploy button this will deploy
all the various services required for
an end to end solution, including an Azure Machine Learning Workspace.
If you wanted to focus specifically on Azure machine experiments
you can just look in the experiment section.
On here you can see I can do used categorization,
online fraud detection, retail forecasting.
Or any other types of experiments from Machine Learning.
Click into one of them and
click deploy and that'll open up an experiment within your Workspace.
We can go to Azure ML directly.
You can just do that through studio.azureml.net.
You can create a free Workspace just using your Microsoft accounts.
I'll create a new experiment here for language detection.
So in this case I have an uploaded data set of various strings
in different languages.
So you can see here all these different languages as my
input data.
And then I'm going to use the detect languages module.
I just connect this.
Select the column and I click run.
All right.
Now that it's finished running we can take a look at the results.
And you can see that it's able to detect what the input text language
is and able to tell you what the language actually is.
So this is a pretty cool Machine Learning experiment.
But now the question is how do we operationalize it?
We can click set up a web service.
And what this will do is create input and output notes.
You can see how the data flows.
And now that I have these notes, I'll just run the experiment again.
These are the inputs and outputs to your rest API.
Now that it's finished running I can click Deploy Web Service.
And now this model is packaged up as an API on top of Azure.
You have an API key and here is your URL for your rest API.
Any app that can call a rest API can hit this.
I'm gonna open up an Excel spreadsheet here.
And you can see how I can make use of this model.
We've created an add-in as part of Excel that allows you to
connect to Azure ML web service.
So now what I can do is just user sample data that I've had.
Send this up to my Machine Learning model and
it will show me the output.
And you can see here that it's able to detect the various languages.
I have some additional strings here just to give you an idea of
the other languages that can be detected.
And we'll send this up to the model.
And as you can see even in languages such as Dutch,
Finnish, even Swahili, or Hebrew, or
Polish this is one way you can interface with your API.
So with Excel you can call the rest API and get your responses back.
Or you can integrate this as part of your end to end solution for
predictive analytics.
You can see how customers such as Rolls-Royce uses Machine Learning
models in their end to end solutions today.
So go ahead and give Azure Machine Learning a try.
You can go to studio.azureml.net, create a free Workspace, and
build your own Machine Learning model.
Open up something in the Cortana Intelligence Suite Gallery and
then get started very quickly.
Thank you very much for your time.
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